DFO Salmon Net Second Survey
Scott Akenhead
2018 February 07
- Introduction
- Survey Data
- Analysis
- Determine Thorough Answers re IYS Topics
- Compare Responders to Recipients by Job Type
- Compare Responders to Recipients by Region
- Survey Code Substitution
- Examination of pattern of skips.
- Interpretation of Effect of Skips
- Barplot of skips from useful but incomplete choices about IYS topics.
- Summary of Choices for IYS Topics.
- Pattern of Choices
- Summary by IYS Theme
- Interpretation
- Scores by Region and Job Classification
- Score by Importance
- Matrix Multiply
- Score by IYS Theme
- Increased Contrast
- Scores by Job
- Heat Maps by Job Type and Region
- Within in the Pacific Region
Introduction
(to be written) ## DFO Salmon Network reflects the International Year of the Salmon (to be written)
## Surveys for the DFO Salmon Network
The survey was named DFO Salmon Net: People and Projects with 351 selected recipients from within DFO. Recipients were contacted by email 2017 September and October. A PDF of the survey questions is posted HERE. A .csv file with 163 responses was downloaded from Survey Monkey after the survey was closed 2017 October 29. Many responses were incomplete.
Survey Data
Raw Data and Ancillary Data
The raw survey data is HERE. Hand edits were required to reorganize information into the appropriate fields, create comma separated lists for complicated results (e.g. lists of URLs, items with commas in quotes, fix spelling, expand abbreviations, standardize capital letters (fewer), and remove spurious text. The resulting sheet is HERE.
The survey results are in condensed format and replaces the text for multiple choice answers with numeric codes referring to the user’s choice, with the translations in yet more Google Sheets: Codes, Place Address and IYS Themes and Topics. Supplementary data, not obtained from the survey, includes Person Details.
All the data is held in Google Sheets to support collaboration and prevent loss. Sheets have roll-back, so all preceding versions (automated) can be recovered.
Read IYS topics and choices from Google Sheets
| code | long | short |
|---|---|---|
| 1 | No, or not applicable | no |
| 2 | Yes, but unlikely at present | pending |
| 3 | Yes, I have an activity that would benefit from additional collaboration | activities to share |
| 4 | Yes, I am keen to share data, skills, and/or knowledge with other | knowledge to share |
| 5 | Yes, this collaboration is vital to my work and should be a high priority for DFO | vital topic |
Read “Edited Survey 2017 October 29”
| firstName | lastName | webPage | jobTitle | jobDescription | branchDirectorateSector | locationCode | locationOther | IYS6.99 | activity2Title | activity3Title | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 65 | Beth.Lenentine@dfo-mpo.gc.ca | Beth | Lenentine | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 104 | John.Holmes@dfo-mpo.gc.ca | John | Holmes | NA | Division Manager, Aquatic Resources, Research and Assessment Division | aka Stock Assessment Division | Science Branch, “Aquatic Resources, Research, and Assessment Division”, Ecosystems and Oceans Science | 12 | NA | NA | NA | NA |
| 114 | Laura.Brown@dfo-mpo.gc.ca | Laura | Brown | http://www.cowichanwatershedboard.ca/topcat/about, http://www.nserc-crsng.gc.ca/Students-Etudiants/PD-NP/Laboratories-Laboratoires/FO-PO_eng.asp | South Coast Area Director | NA | Fisheries and Aquaculture Management Division | 18 | NA | NA | NA | NA |
| 119 | Lyse.Godbout@dfo-mpo.gc.ca | Lyse | Godbout | NA | Research Biologist | NA | Science Branch,“Aquatic Resources, Research, and Assessment Division”, Quantitative Assessment Methods Section | 12 | NA | NA | Size-selective Mortality and Early Marine Growth | Climate and Juvenile Salmon |
| 126 | Mary.Thiess@dfo-mpo.gc.ca | Mary | Thiess | NA | NA | Acting Head, Salmon Assessment | Science Branch, “Aquatic Resources, Research, and Assessment Division”, Salmon Assessment | 12 | NA | NA | NA | NA |
Read Person with Region and Job Code
This ancillary table has more names than the survey recipients.
| name | region | jobCode | telephone | place | |
|---|---|---|---|---|---|
| 17 | Andrew Pereboom | Pacific | EG | 250-286-5884 | Campbell River |
| 60 | Carmel Lowe | Pacific | MA | NA | NA |
| 85 | Craig Keddy | Maritimes | HA | 902-679-5572 | NA |
| 153 | Ivan Winther | Pacific | BI | 250-627-3459 | Prince Rupert Field Office |
| 158 | Jamie Scroggie | Pacific | RM | 250-851-4878 | NA |
| 193 | Julia Bradshaw | Pacific | EG | 250-756-7054 | NA |
| 260 | Michael Folkes | Pacific | BI | 250-756-7264 | NA |
| 294 | Peter Hall | Pacific | RM | 250-720-4445 | NA |
| 303 | Rick Rempel | Pacific | EG | 604-666-0691 | NA |
| 308 | Rob Schaefer | Pacific | HA | 604-814-1070 | NA |
| code | shortJobName | longJobName |
|---|---|---|
| MA | Manager | Manager (people manager, not resource/fishery manager) |
| EG | Technician | Technical (technician, engineer) |
| RM | Resource Manager | Resource Manager (includes biologists and tech’s whose primary role is wrt fisheries management |
| PO | Policy or Economics | Policy analyst, economist |
| BI | Biologist | Biologist (science) |
| RE | Research Scientist | Research Scientist |
| HA | Enhancement | Biologists, community advisors, managers and technicians working on enhancement |
| ## Read | Recipients | |
| Obtaine | d from Survey Monkey, | people who actually received the survey and two reminders. |
| name | responded | ||
|---|---|---|---|
| 1 | Aaron Burgoyne | Aaron.Burgoyne@dfo-mpo.gc.ca | No |
| 53 | Carlos Martinez | Carlos.Martinez@dfo-mpo.gc.ca | No |
| 84 | Darren Goetze | Darren.Goetze@dfo-mpo.gc.ca | No |
| 105 | Eddy Kennedy | Eddy.Kennedy@dfo-mpo.gc.ca | Complete |
| 112 | Frank Corbett | Frank.Corbett@dfo-mpo.gc.ca | Complete |
| 163 | Joan Bennett | Joan.Bennett@dfo-mpo.gc.ca | No |
| 215 | Les Clint | Les.Clint@dfo-mpo.gc.ca | Partial |
| 246 | Merv Mochizuki | Merv.Mochizuki@dfo-mpo.gc.ca | No |
| 282 | Pieter Van Will | Pieter.VanWill@dfo-mpo.gc.ca | No |
| 302 | Sandy Devcic | Sandra.Devcic@dfo-mpo.gc.ca | Complete |
Extract Names for IYS Theme and Topic
Lists of theme and topic names, and a variable fctr that relates the 37 topics to the 6 themes.
j <- IYSCodeIdea$IYS_Row == 0 # theme
print("Theme, Short")
theme=IYSCodeIdea[j,"IYS_Short_Text"] %T>% print;
j <- j | IYSCodeIdea$IYS_Row == 99 # theme or "other"
print("Topic, Short")
topic <- IYSCodeIdea[!j,"IYS_Short_Text"] %T>% print; # not theme, not "other"
print("\nRelate 37 Topics to 6 Themes")
fctr <- IYSCodeIdea[!j,"IYS_Theme"] %T>% print [1] "Theme, Short"
[1] "IYS.1 Status Salmon and Habitats"
[2] "IYS.2 Effects of Changing Habitats"
[3] "IYS.3 New tech and methods"
[4] "IYS.4 Connecting Salmon to People"
[5] "IYS.5 Information Systems"
[6] "IYS.6 Outreach and Communication"
[1] "Topic, Short"
[1] "IYS.1.1 Field Data"
[2] "IYS.1.2 Data Analysis"
[3] "IYS.1.3 Fishery Management, Assessment"
[4] "IYS.1.4 Stock Status Assessment"
[5] "IYS.1.5 Habitat Assessment"
[6] "IYS.1.6 Population identification"
[7] "IYS.1.7 Marine Survival, Growth, Migration"
[8] "IYS.1.8 Interactions: Wild, Hatchery, Farmed"
[9] "IYS.1.9 Toxicology"
[10] "IYS.2.1 Freshwater habitats"
[11] "IYS.2.2 Marine and Estuarine Habitats"
[12] "IYS.2.3 Climate and Ecosystem Models"
[13] "IYS.2.4 Adaptation"
[14] "IYS.2.5 Policy and Management"
[15] "IYS.3.1 Field methods"
[16] "IYS.3.2 Individual fish"
[17] "IYS.3.3 Fisheries management process"
[18] "IYS.3.4 New analyses"
[19] "IYS.3.5 Advances genetics, genomics"
[20] "IYS.3.6 Science management"
[21] "IYS.3.7 Implementation"
[22] "IYS.4.1 First Nations Opportunities"
[23] "IYS.4.2 Benefits from Salmon"
[24] "IYS.4.3 Community engagement"
[25] "IYS.4.4 Better science communication"
[26] "IYS.4.5 Traditional ecological knowledge"
[27] "IYS.4.6 Young scientists"
[28] "IYS.4.7 Changing role of salmon in societies"
[29] "IYS.5.1 Database Integration"
[30] "IYS.5.2 Knowledge management"
[31] "IYS.5.3 Data sharing arrangements"
[32] "IYS.5.4 Data visualization"
[33] "IYS.6.1 International projects"
[34] "IYS.6.2 Celebrating success"
[35] "IYS.6.3 Outreach methods, awareness"
[36] "IYS.6.4 Engagement FM to science to FM"
[37] "IYS.6.5 Linking salmon to climate change"
[1] "\nRelate 37 Topics to 6 Themes"
[1] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 6 6 6
[36] 6 6Merge Person Details with Recipients
A list of survey recipients was recovered from Survey Monkey, provided name, email, and response (complete,partial,no) for 351 recipients and 163 responses. A separate list of 368 DFO salmon staff was compiled with name, email, region, and job type (7 categories). These lists were merged. Misspelt names were discovered and corrected in the longer staff list.
# person is 367 by 9. emails are all in caps, unlike recipient
# recipient is 351 by 3.
person1 = merge(x=person[,c(1,2,4)], y=recipient, by="name",all.y=TRUE); # 351
person1$email.x <- NULL; # remove a column
x=person1[ (is.na(person1$region) | is.na(person1$jobCode)), ];
if(nrow(x) != 0) {print(x[1:max(nrow(x),10)])} else print("No job or region missing") # check![1] "No job or region missing"Region and Job as Factors for Survey
The survey data with 163 names was merged with ancilliary data for 351 names, using email address as the unique identifier in both tables. Variables name,region, and jobCode are merged into survey.
Capital letters in emails vary between lists: person has names as proper nouns but not all emails in survey have names with capital letters. This was solved by merging person with recipient so person1 had the same capitals for email as survey.
# survey=survey[order(survey$email),] # sort, vary caps in email have no effect
# person1=person1[order(person1$email),] # 351 rows sort ditto
survey1=merge(survey,person1[,c(1:4)],by="email",all.x=T ) # 163 rows
# not all emails in survey are capitalized.
# write.csv(file="test.csv",survey1[,c(1:3,76:78)]) # checked!Analysis
Determine Thorough Answers re IYS Topics
First determine columns for choices re IYS theme and topic, then count missing answers for IYS topics for each response. That results is used for the frequency of responses by job type and region. x0 is a matrix, 163 by 37.
x1 is 124 by 37 after deleting not-useful responses, nar. y0 is a data.frame,163 by 40 after adding columns name, region, jobType.
k = colnames(survey1) %>% substr(1,3) %>% equals("IYS"); # cols 10 to 52, so 43
k1= colnames(survey1) %>% substr(5,7) %>% equals(".99"); # 6 of IYS "other"
k=k & !k1; # leaves 37 columns
x0 = survey1[ ,k] %>% as.matrix; # 163 row, 37 col
y0 <- cbind(x0, survey1[76:78]) # add name,region,job to IYS choices
nar = apply(x0,1, function(x) sum(is.na(x)) ) # count missing by person (row)
jans <- nar > (37-25) # rows with < 25 answers to 37 topics, junk answers
thorough = rep("Thorough",163);
thorough[jans] <- "Partial"; # 39, leaves 124
x1 <- x0[!jans,] # useful choices
y1 <- y0[!jans,] # with factors
nac <- x1 %>% apply(2, function(x) sum(is.na(x)) ) # count missing by topic
names(nac)= colnames(x0)
nacIYS1.1 IYS1.2 IYS1.3 IYS1.4 IYS1.5 IYS1.6 IYS1.7 IYS1.8 IYS1.9 IYS2.1
0 3 2 2 2 2 4 4 4 0
IYS2.2 IYS2.3 IYS2.4 IYS2.5 IYS3.1 IYS3.2 IYS3.3 IYS3.4 IYS3.5 IYS3.6
1 2 2 1 0 2 2 2 2 1
IYS3.7 IYS4.1 IYS4.2 IYS4.3 IYS4.4 IYS4.5 IYS4.6 IYS4.7 IYS5.1 IYS5.2
2 1 0 1 0 1 1 2 1 0
IYS5.3 IYS5.4 IYS6.1 IYS6.2 IYS6.3 IYS6.4 IYS6.5
0 0 1 0 0 1 0 Compare Responders to Recipients by Job Type
The survey was sent to 351 people, of which 163 responded and 124 provided useful choices about collaboration on topics within IYS themes. The job types for recipients were counted and compared to responders, and similarly for DFO regions. The 351 responses also had to have a count by person (row) of missing choices re IYS topics to determine “not thorough” responses.
rrp=c("Recipients","Responders","Thorough","Percent")
responded <- person1$responded
responded[responded == "Opted out"] <- "No"
responded[ person1$responded== "Partial" | person1$responded == "Complete"] <- "Yes";
# nar is 163 long from survey responses not 351 from recipients.
jj <- 0;
for(j in 1:length(responded)){ # 351
if(responded[j] == "Yes") {
jj=jj+1;
responded[j] <- thorough[jj] # 163 things in 351 places
}
}
jobFreq <- table(person1$jobCode, responded) %>% addmargins
a <- as.matrix(jobFreq)
PercentThorough <- round( 100*a[,3]/a[,4], 0)
bj=cbind(a,PercentThorough)
kable(bj,caption="Table x. Survey Response by Job Type across all Regions")| No | Partial | Thorough | Sum | PercentThorough | |
|---|---|---|---|---|---|
| BI | 35 | 8 | 27 | 70 | 39 |
| EG | 53 | 9 | 21 | 83 | 25 |
| HA | 17 | 3 | 16 | 36 | 44 |
| MA | 18 | 6 | 11 | 35 | 31 |
| PO | 9 | 1 | 5 | 15 | 33 |
| RE | 8 | 4 | 9 | 21 | 43 |
| RM | 48 | 8 | 35 | 91 | 38 |
| Sum | 188 | 39 | 124 | 351 | 35 |
Compare Responders to Recipients by Region
region = person1$region
region[region == "Newfoundland"] <- "NL";
region[region == "Central and Arctic"] <- "Central";
region[region == "HQ"] <- "NCR";
regFreq = table(region,responded) %>% addmargins
a <- as.matrix(regFreq)
PercentThorough <- round( 100*a[,3]/a[,4], 0)
br=cbind(a,PercentThorough)
kable(br,caption="Table x. Survey Response by Region across all Job Types")| No | Partial | Thorough | Sum | PercentThorough | |
|---|---|---|---|---|---|
| Central | 1 | 0 | 1 | 2 | 50 |
| Gulf | 12 | 2 | 7 | 21 | 33 |
| Maritimes | 25 | 6 | 12 | 43 | 28 |
| NCR | 3 | 0 | 2 | 5 | 40 |
| NL | 5 | 2 | 4 | 11 | 36 |
| Pacific | 140 | 28 | 94 | 262 | 36 |
| Quebec | 2 | 1 | 4 | 7 | 57 |
| Sum | 188 | 39 | 124 | 351 | 35 |
Summarize as plots.
SetPar(); par(xaxs="r",mgp = c(2,0.5,0))
colr=c("grey","cyan","magenta")
txt=c("Thorough","Partial","No")
barplot(t(jobFreq[1:7,1:3]), col=colr, beside=F, las=1, ylim=c(0,100),
xlab="Job Type", ylab="Count");
box();axis(4,labels=F)
legend("top",legend=txt, fill=rev(colr), bty="n")
Survey recipients and responders compared by job type. MA: Manager (primarily manage staff), EG: Technicians and Engineers (not in hatcheries), RM: Resource Manager (fisheries and habitat management by biologists, technicians, and managers), PO: Policy analysts and economists, BI: Biologist (Science Branch), RE: Research Scientist, HA: Enhancement Staff (biologists, community advisors, hatcheries staff).
SetPar(); par(xaxs="r",mgp = c(2,0.5,0))
barplot(t(regFreq[1:7,1:3]), col=colr, beside=F, las=2, ylim=c(0,275),
xlab="", ylab="Count");
box();axis(4,labels=F)
legend("top",legend=txt, fill=rev(colr), bty="n")
Survey recipients and responders compared by DFO Region. Central: Central and Arctic, NCR: National Capital Region, NFLD: Newfoundland and Labrador,
Survey Code Substitution
Survey choices about IYS topics can be expanded to short names and long names for displays. Similarly, codes for job, region, and location (building) can be expanded. A function to determine the correct row of names in IYSCodeIdea is required. The actual choice, 1 to 5, is the row in IYSCodeChoice. If all IYS choices were missing, that response was deleted. If some choices were missing, lack of interest was assumed (assigned choice = 1).
# count missing in each column
nar = x0 %>% apply(1, function(x) sum(is.na(x)) )
# count missing in each row
jans = nar > (37-25) # rows with < 25 answers to 37 topics, junk answersExamination of pattern of skips.
For IYS topics, 34 survey responders did not address any IYS topics" and 39 addressed fewer than 25 topics. Those responders were deleted before the analysis of collaboration potential.
Table x. Frequency of skipped choices by IYS topic.
a=tapply(nac,theme[fctr],sum) # sum missing by theme for the 37 topics
b0 <- table(fctr)
b1=(a / b0) %>% round(.,1) # skips per topic by theme
b2=cbind(a,b0,b1)
colnames(b2) <- c("Skips","Topics","Skips/Topics")
kable(b2, caption ="Table x. Frequency of skipped choices by IYS topic.")| Skips | Topics | Skips/Topics | |
|---|---|---|---|
| IYS.1 Status Salmon and Habitats | 23 | 9 | 2.6 |
| IYS.2 Effects of Changing Habitats | 6 | 5 | 1.2 |
| IYS.3 New tech and methods | 11 | 7 | 1.6 |
| IYS.4 Connecting Salmon to People | 6 | 7 | 0.9 |
| IYS.5 Information Systems | 1 | 4 | 0.2 |
| IYS.6 Outreach and Communication | 2 | 5 | 0.4 |
Interpretation of Effect of Skips
Responders who made choices for 25 or more, but not all, of the IYS topics did not tend to quit before the last themes. The opposite is true, there were more skips in the first theme than in subsequent themes. The skips were spread evenly across the topics within theme 1 and theme 3 (34 of the 49 skips). From 124 useful resposes to 37 topic, there are 4,588 choices, of which 49 skips is 1%. We concluded that the pattern of skips would not introduce an important bias if we were wrong about interpreting a skip to mean that a topic was No, or not applicable to a responder’s interest in potential collaborations, i.e., that a skip is the same as choice = 1. In this situation, not making a choice was a meaningful choice.
Barplot of skips from useful but incomplete choices about IYS topics.
x=nac[j <- nac > 0] # skips in topics where there were skips
SetPar();par(xaxs="r",oma = c(3,2,1,1));
barplot(x, names.arg=topic[j],las=2,ylim=c(0,5),ylab="Count",xlab="",cex.names = 0.33)
box();
Count of skips for IYS Topics where skips occurred, within the 124 survey responses considered useful.
x=nac #
SetPar();par(xaxs="r",oma = c(3,2,1,1));
barplot(x, names.arg=topic,las=2,ylim=c(0,5),ylab="Count",xlab="",cex.names = 0.33)
box();
Count of skips for IYS Topics where skips occurred, including topics with zero skips, within the 124 survey responses considered useful.
x=nac # total choice in topics including topics with zero skips
SetPar();par(xaxs="r",oma = c(3,2,1,1));
barplot(124-x, names.arg=topic,las=2,ylim=c(0,124),
ylab="Count",xlab="",cex.names = 0.33); box();
Totals for choice in topics, within the 124 survey responses considered useful.
Summary of Choices for IYS Topics.
A table with the count of choices for each topic is created (37 rows, 5 columns) by a local function ChoiceTabSum.
x1[is.na(x1)] <- 1; # skipped -> Choice 1, "not applicable"
y1[is.na(y1)] <- 1;
x2 = x1 %>% apply(2,ChoiceTabSum); # by column. 5 by 37
colnames(x2) <- topic
x2 <- t(x2); # a matrix, 37 by 5
choice=c("1. No","2. Pending","3. Activities","4. Knowledge","5. Vital");
kable(x2, col.names=choice, caption="Counts of choices regarding collaboration by IYS topic, from 124 thorough responses.");
Terse(x2) # to convert text to table in Word.| 1. No | 2. Pending | 3. Activities | 4. Knowledge | 5. Vital | |
|---|---|---|---|---|---|
| IYS.1.1 Field Data | 20 | 19 | 29 | 23 | 33 |
| IYS.1.2 Data Analysis | 19 | 29 | 26 | 30 | 20 |
| IYS.1.3 Fishery Management, Assessment | 25 | 23 | 31 | 20 | 25 |
| IYS.1.4 Stock Status Assessment | 30 | 31 | 27 | 22 | 14 |
| IYS.1.5 Habitat Assessment | 35 | 37 | 22 | 16 | 14 |
| IYS.1.6 Population identification | 37 | 25 | 28 | 16 | 18 |
| IYS.1.7 Marine Survival, Growth, Migration | 33 | 16 | 33 | 17 | 25 |
| IYS.1.8 Interactions: Wild, Hatchery, Farmed | 36 | 28 | 23 | 14 | 23 |
| IYS.1.9 Toxicology | 67 | 28 | 20 | 5 | 4 |
| IYS.2.1 Freshwater habitats | 29 | 36 | 29 | 16 | 14 |
| IYS.2.2 Marine and Estuarine Habitats | 28 | 40 | 28 | 12 | 16 |
| IYS.2.3 Climate and Ecosystem Models | 33 | 42 | 29 | 11 | 9 |
| IYS.2.4 Adaptation | 32 | 44 | 23 | 13 | 12 |
| IYS.2.5 Policy and Management | 31 | 45 | 21 | 17 | 10 |
| IYS.3.1 Field methods | 21 | 31 | 26 | 23 | 23 |
| IYS.3.2 Individual fish | 36 | 30 | 25 | 20 | 13 |
| IYS.3.3 Fisheries management process | 33 | 36 | 23 | 18 | 14 |
| IYS.3.4 New analyses | 38 | 37 | 26 | 14 | 9 |
| IYS.3.5 Advances genetics, genomics | 46 | 29 | 28 | 12 | 9 |
| IYS.3.6 Science management | 31 | 30 | 30 | 19 | 14 |
| IYS.3.7 Implementation | 22 | 24 | 36 | 27 | 15 |
| IYS.4.1 First Nations Opportunities | 18 | 30 | 31 | 18 | 27 |
| IYS.4.2 Benefits from Salmon | 24 | 42 | 26 | 13 | 19 |
| IYS.4.3 Community engagement | 22 | 33 | 26 | 20 | 23 |
| IYS.4.4 Better science communication | 14 | 32 | 28 | 25 | 25 |
| IYS.4.5 Traditional ecological knowledge | 31 | 42 | 25 | 12 | 14 |
| IYS.4.6 Young scientists | 31 | 28 | 35 | 19 | 11 |
| IYS.4.7 Changing role of salmon in societies | 51 | 37 | 16 | 12 | 8 |
| IYS.5.1 Database Integration | 23 | 33 | 34 | 16 | 18 |
| IYS.5.2 Knowledge management | 25 | 34 | 36 | 16 | 13 |
| IYS.5.3 Data sharing arrangements | 19 | 37 | 33 | 16 | 19 |
| IYS.5.4 Data visualization | 26 | 35 | 35 | 13 | 15 |
| IYS.6.1 International projects | 30 | 42 | 30 | 11 | 11 |
| IYS.6.2 Celebrating success | 28 | 36 | 26 | 20 | 14 |
| IYS.6.3 Outreach methods, awareness | 26 | 38 | 28 | 18 | 14 |
| IYS.6.4 Engagement FM to science to FM | 22 | 31 | 31 | 21 | 19 |
| IYS.6.5 Linking salmon to climate change | 22 | 47 | 24 | 13 | 18 |
IYS.1.1 Field Data,20,19,29,23,33
IYS.1.2 Data Analysis,19,29,26,30,20
IYS.1.3 Fishery Management, Assessment,25,23,31,20,25
IYS.1.4 Stock Status Assessment,30,31,27,22,14
IYS.1.5 Habitat Assessment,35,37,22,16,14
IYS.1.6 Population identification,37,25,28,16,18
IYS.1.7 Marine Survival, Growth, Migration,33,16,33,17,25
IYS.1.8 Interactions: Wild, Hatchery, Farmed,36,28,23,14,23
IYS.1.9 Toxicology,67,28,20,5,4
IYS.2.1 Freshwater habitats,29,36,29,16,14
IYS.2.2 Marine and Estuarine Habitats,28,40,28,12,16
IYS.2.3 Climate and Ecosystem Models,33,42,29,11,9
IYS.2.4 Adaptation,32,44,23,13,12
IYS.2.5 Policy and Management,31,45,21,17,10
IYS.3.1 Field methods,21,31,26,23,23
IYS.3.2 Individual fish,36,30,25,20,13
IYS.3.3 Fisheries management process,33,36,23,18,14
IYS.3.4 New analyses,38,37,26,14,9
IYS.3.5 Advances genetics, genomics,46,29,28,12,9
IYS.3.6 Science management,31,30,30,19,14
IYS.3.7 Implementation,22,24,36,27,15
IYS.4.1 First Nations Opportunities,18,30,31,18,27
IYS.4.2 Benefits from Salmon,24,42,26,13,19
IYS.4.3 Community engagement,22,33,26,20,23
IYS.4.4 Better science communication,14,32,28,25,25
IYS.4.5 Traditional ecological knowledge,31,42,25,12,14
IYS.4.6 Young scientists,31,28,35,19,11
IYS.4.7 Changing role of salmon in societies,51,37,16,12,8
IYS.5.1 Database Integration,23,33,34,16,18
IYS.5.2 Knowledge management,25,34,36,16,13
IYS.5.3 Data sharing arrangements,19,37,33,16,19
IYS.5.4 Data visualization,26,35,35,13,15
IYS.6.1 International projects,30,42,30,11,11
IYS.6.2 Celebrating success,28,36,26,20,14
IYS.6.3 Outreach methods, awareness,26,38,28,18,14
IYS.6.4 Engagement FM to science to FM,22,31,31,21,19
IYS.6.5 Linking salmon to climate change,22,47,24,13,18
[1] ""Pattern of Choices
Without rearranging the IYS topics, the pattern of collaboration choices (1 to 5) is presented. Darker magenta are the most frequent choices, darker cyan are the least frequent.
z=x2[37:1,5:1] # I have not idea why this flip is required.
IYSplot(z) # uses default text and titles.
Collaboration Choices by IYS Topic, fig.height=6
Summary by IYS Theme
From all 37 choices (rows), the count for each of the five choices (columns) were summarized within each of the 6 IYS themes. Because there were varying number of topics (rows) within themes, the average was appropriate rather than the sum. The resulting table with 6 rows is also presented as an image via custom function IYSplot.
# x2 is the 37 by 5 matrix and fctr relates topic to theme
xtheme=matrix(nrow=6,ncol=5); # 6 themes by 5 choices
for(j in 1:5) xtheme[,j] = tapply(x2[,j],fctr,mean) # by column
row.names(xtheme) = theme
kable(round(xtheme,0), col.names=1:5, caption="Table x. Collaboration choices by IYS theme from all useful survey responses.")
#print("Choices by Theme, scaled as -1 to +1")
#(((xtheme - min(xtheme)) / (max(xtheme)-min(xtheme) ) -.5)*2) %>% round(1) # -1 to 1.
z=xtheme[6:1,5:1] # again with the freakin' flip. WHT?!
IYSplot(z,ytxt=theme,ylab="IYS Theme")
Figure x. Collaboration choices by IYS theme from all useful survey responses.
# heatmap(t(xt0), Rowv=NA,Colv=NA)
#cm= xt0 %>% rowMeans; cm; # 5
#xta = xt0 %>% apply(2, function(x) x-cm); xta %>% round(0) %>% print;| 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|
| IYS.1 Status Salmon and Habitats | 34 | 26 | 27 | 18 | 20 |
| IYS.2 Effects of Changing Habitats | 31 | 41 | 26 | 14 | 12 |
| IYS.3 New tech and methods | 32 | 31 | 28 | 19 | 14 |
| IYS.4 Connecting Salmon to People | 27 | 35 | 27 | 17 | 18 |
| IYS.5 Information Systems | 23 | 35 | 34 | 15 | 16 |
| IYS.6 Outreach and Communication | 26 | 39 | 28 | 17 | 15 |
Interpretation
Whilst pondering survey reponses about IYS topics and themes, please note choices within a topic were exclusive. This excluded the ability for a survey responder to plead for help to obtain collaboration for their activities: Choice 2 Yes, but unlikely at present precluded choice 5 This collaboration is vital to my work and should be a priority for DFO. As a result, people who answered the survey humbly mentioned that they were unable to pursue interesting opportunities for collaboration, presumably due to resource constraints (workload, staff, budget), instead of stridently asserting that DFO needs to help them with the collaborations necessary to modernize their activities. Perhaps polite Canadians, perhaps a flaw in the survey design. This exclusion effect applies to IYS Theme 2 Effects of Changing Habitats and to IYS Theme 6 Outreach and Communication (see strong magenta for choice 2 matched with cyan for choice 5 for themes 2 and 6). It’s the faint calls for help that need attention.
With the caveat that topics from different themes were associated with specific collaboration opportunities for survey responders, and that topics within themes might not all be associated that way, this summary of topics by themes is offered:
* Theme 1 (current status of salmon) was voted to be not applicable for collaboration.
* Theme 2 (effects of changing habitats) was strongly interested but cannot pursue
* Theme 3 (new tech and methods) was moderately not applicable and cannot pursue.
* Theme 4 (connecting salmon to people) was clearly interested but cannot pursue.
* Theme 5 (information systems) was a bit more positive, many people were interested and many had projects that needed collaboration re information management and knowledge mobilization.
* Theme 6 (outreach and education), was similar to Theme 2, clearly but less emphatically interested but cannot pursue.
After subtracting the column means, the overall tendency for choices 1 through 5, the preceding summary for Theme 2 was reinforced. This treatment emphasized that Theme 5 (information systems) was something DFO staff wanted for their existing activities.
It is worth noting that the conclusions from votes about collaboration on 37 topics are also the conclusions from summarizing those votes into 6 themes. Restating that conclusion in this context, and guessing at the story behind the numbers: The fact that salmon are facing a changing world (climate change in the Salmosphere) is important to DFO staff, but they need help with data management before they can react. This preliminary and arguable conclusion will be addressed in subsequent analyses of the survey.
Humans also face a rapidly changing world. We need to understand that in order to react wisely, hence the interest in salmon as the canary in the coal mine but on a global scale. If the canaries die (from carbon monixide), then get out of the coal mine, fast. If the salmon die (from global warming), then … ah, nowhere to run.
Scores by Region and Job Classification
A breakdown by region and then job of responses (Complete, useful, not useful) is followed by a similar breakdown after scoring responses. The weights for scoring are: choice 1 (not applicable): 0, choice 2 (deferred): 1, choice 3 (offer activity): 2, choice 4 (offer knowledge): 3, choice 5 (critical, a DFO priority): 4. The intention is to identify the importance of an IYS topic across all the choices.
Score by Importance
The choices for IYS topics are approximately ordinal, from choice 1 which indicates minimal importance to choice 5 which indicates maximal importance. Here the choices by IYS topic were treated as measurements of importance (as rational numbers) supplied by the survey responders, and applied as weights to guage interest in collabortion on specific topics. Setting “not applicable” to a weight of 0 was straightforward. More arbitrary was choosing a quantitative difference to establish the contrast between choice “2: interesting but not immediately applicable” (perhaps for want of resources) and choice “5: vital to my work and should be a high priority for DFO.” Choices with labels “1” to “5” could be given weights 0 to 4 or weights (0 to 4)2 or another weighting. Apart from ranking all of the topics, it was worth noticing topics frequently considered vital and high priority.
Choosing (voting) that a collaboration topic is “critical to my work and should be a high priority” was given 4X the weight of “interesting but unlikely” and the choice “not applicable” did not directly contribute to the scoring, but of course removed a count from another choice.
Results were biased from the initial selection of 361 survey recipients, from self-selection by the 1/3 that responded to at least 25 of the IYS topics, and perhaps subtley from from the order of the IYS themes and topics (although there was no tendancy to skip the last themes). Conversely, people who responded enthusiastically and thoroughly to survey about collaboration potential may be a representative sample (or the population!) of DFO staff who will be epicenters for future collaborations re salmon.
Matrix Multiply
The choices “1” to “5” were weighted 0 to 4, and each row of a matrix of counts of choices (37 rows, 5 cols) is replaced by its weighted sum.
# x0 is 163 by 37, y0 is 163 by 40 with name, region, jobcode.
# x1 has only usefulresponses, 124 by 37. Ditto y1, 124 by 40.
# x2 is the count of choices (1 to 5) by 124 responders, so 37 topics by 5 choices.
# %*% is matrix multipy
x3 <- x2 %*% (0:4) %>% ScaleTo10;
j1 <- order(x3, decreasing = T)
(data.frame(Topic=topic[j1], Score = x3[j1])) %T>% kable(caption="Table x. Relative Importance of IYS Topics, scored by 124 survey choices, then scaled from 0 to 100");# %T>% Terse Topic Score
1 IYS.1.1 Field Data 10
2 IYS.4.1 First Nations Opportunities 9
3 IYS.4.4 Better science communication 9
4 IYS.1.2 Data Analysis 8
5 IYS.1.3 Fishery Management, Assessment 8
6 IYS.3.1 Field methods 8
7 IYS.3.7 Implementation 8
8 IYS.4.3 Community engagement 8
9 IYS.1.7 Marine Survival, Growth, Migration 7
10 IYS.5.1 Database Integration 7
11 IYS.5.3 Data sharing arrangements 7
12 IYS.6.4 Engagement FM to science to FM 7
13 IYS.1.4 Stock Status Assessment 6
14 IYS.1.6 Population identification 6
15 IYS.1.8 Interactions: Wild, Hatchery, Farmed 6
16 IYS.2.1 Freshwater habitats 6
17 IYS.3.6 Science management 6
18 IYS.4.2 Benefits from Salmon 6
19 IYS.4.6 Young scientists 6
20 IYS.5.2 Knowledge management 6
21 IYS.5.4 Data visualization 6
22 IYS.6.2 Celebrating success 6
23 IYS.6.3 Outreach methods, awareness 6
24 IYS.6.5 Linking salmon to climate change 6
25 IYS.1.5 Habitat Assessment 5
26 IYS.2.2 Marine and Estuarine Habitats 5
27 IYS.3.2 Individual fish 5
28 IYS.3.3 Fisheries management process 5
29 IYS.4.5 Traditional ecological knowledge 5
30 IYS.2.3 Climate and Ecosystem Models 4
31 IYS.2.4 Adaptation 4
32 IYS.2.5 Policy and Management 4
33 IYS.3.4 New analyses 4
34 IYS.6.1 International projects 4
35 IYS.3.5 Advances genetics, genomics 3
36 IYS.4.7 Changing role of salmon in societies 2
37 IYS.1.9 Toxicology 0Score by IYS Theme
The mean within themes of scores for topics.
x3.theme= x3 %>% tapply(fctr,mean) %>% ScaleTo10;
j=order(x3.theme, decreasing = T)
(data.frame(theme[j],x3.theme[j])) %>% kable(col.names=c("Theme", "Score"))| Theme | Score | |
|---|---|---|
| 4 | IYS.4 Connecting Salmon to People | 10 |
| 5 | IYS.5 Information Systems | 10 |
| 1 | IYS.1 Status Salmon and Habitats | 9 |
| 6 | IYS.6 Outreach and Communication | 6 |
| 3 | IYS.3 New tech and methods | 5 |
| 2 | IYS.2 Effects of Changing Habitats | 0 |
Increased Contrast
To observed the effect of a different weighting, scoring and sorting was repeated with increased contrast, using weights = 0, 1, 4, 9, 16. Choice “5: critical” was thereby 4X more influential than with the previous scoring.
x31 = x2 %*% (0:4)^2 %>% ScaleTo10;
j2= order(x31,decreasing=TRUE);
data.frame(Topics=topic[j2], Score = x31[j2]) %>% kable;# %T>% Terse;| Topics | Score |
|---|---|
| IYS.1.1 Field Data | 10 |
| IYS.1.2 Data Analysis | 8 |
| IYS.1.3 Fishery Management, Assessment | 8 |
| IYS.3.1 Field methods | 8 |
| IYS.4.1 First Nations Opportunities | 8 |
| IYS.4.4 Better science communication | 8 |
| IYS.1.7 Marine Survival, Growth, Migration | 7 |
| IYS.3.7 Implementation | 7 |
| IYS.4.3 Community engagement | 7 |
| IYS.6.4 Engagement FM to science to FM | 7 |
| IYS.1.8 Interactions: Wild, Hatchery, Farmed | 6 |
| IYS.5.1 Database Integration | 6 |
| IYS.5.3 Data sharing arrangements | 6 |
| IYS.1.4 Stock Status Assessment | 5 |
| IYS.1.6 Population identification | 5 |
| IYS.2.1 Freshwater habitats | 5 |
| IYS.2.2 Marine and Estuarine Habitats | 5 |
| IYS.3.2 Individual fish | 5 |
| IYS.3.3 Fisheries management process | 5 |
| IYS.3.6 Science management | 5 |
| IYS.4.2 Benefits from Salmon | 5 |
| IYS.4.6 Young scientists | 5 |
| IYS.5.2 Knowledge management | 5 |
| IYS.5.4 Data visualization | 5 |
| IYS.6.2 Celebrating success | 5 |
| IYS.6.3 Outreach methods, awareness | 5 |
| IYS.6.5 Linking salmon to climate change | 5 |
| IYS.1.5 Habitat Assessment | 4 |
| IYS.4.5 Traditional ecological knowledge | 4 |
| IYS.2.3 Climate and Ecosystem Models | 3 |
| IYS.2.4 Adaptation | 3 |
| IYS.2.5 Policy and Management | 3 |
| IYS.3.4 New analyses | 3 |
| IYS.3.5 Advances genetics, genomics | 3 |
| IYS.6.1 International projects | 3 |
| IYS.4.7 Changing role of salmon in societies | 2 |
| IYS.1.9 Toxicology | 0 |
The order of topics was not appreciably changed by increase contrast. This suggests the order for choice 5 (vital, high priority) determines the order when choices are scored as a weighted sum. The topics with the top nine scores were identical with and without increased contrast in weights; this true for the bottom nine scores. Note this result was not split by job type.
j1[1:10]; j2[1:10]
(j1[ 1:10] %in% j2[ 1:10]) %>% print
j1[28:37]; j2[28:37]
(j1[28:37] %in% j2[28:38]) %>% print [1] 1 22 25 2 3 15 21 24 7 29
[1] 1 2 3 15 22 25 7 21 24 36
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[1] 17 26 12 13 14 18 33 19 28 9
[1] 5 26 12 13 14 18 19 33 28 9
[1] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEScores as a Function
We decided that scores from choices would be important for comparisons of collaboration topics between regions and job-types, and for job-types within Pacific Region.
ScoreTopics = function (a){
# a is a matrix of counts by topics (row) and choices (col).
# fctr, topics, theme: inherited
a1 = a %*% (0:4) %>% ScaleTo10;
j= order(a1,decreasing=TRUE);
data.frame(Topics=topic[j], Score = a1[j]) %T>%
kable(col.names=c("Topic", "Score"));
}
ScoreThemes = function (a){
a1 = a %*% (0:4)
b.theme= a1 %>% tapply(fctr,mean) %>% ScaleTo10;
j=order(b.theme,decreasing = T);
data.frame(Theme=theme[j],Score=b.theme[j]) %>%
kable(col.names=c("Theme", "Score"));
}Scores by Job
The preceding analysis, weighted sums of choices for IYS topics, is repeated for each of the seven job types. Because there are different numbers of respondents for each job type, they result is scaled 0 to 10 within each job type. This removes the effect of differing sample sizes. The result is presented as single table with job type as columns. To prevent confusion (maybe) the result for all job types (last column) is the mean of the scaled scores for each job type (preceding columns), but not scaled 0 to 10. The effect is to assign equal weight to each job type, after scaling.
# x3 is the score for all 124 responses, vector 37
# x1 is 124 by 37
scoreJob <- matrix(nrow = 37,ncol = 8);
for(j in 1:7){
k <- y1$jobCode == jobCode$code[j]; # who has job j
a <- apply(x1[k,], 2, ChoiceTabSum); # count of choices, 5 by 37
scoreJob[,j] <- t(a) %*% (0:4) %>% ScaleTo10; # score
}
scoreJob[,8] <- scoreJob[,1:7] %>% apply(1,mean) %>% round(0) # previous, score for all 124 responses
dimnames(scoreJob)=list(Topic = topic, Job = c(jobCode$code,"All"))
scoreJob %>% kable; # %T>% Terse;| MA | EG | RM | PO | BI | RE | HA | All | |
|---|---|---|---|---|---|---|---|---|
| IYS.1.1 Field Data | 5 | 10 | 8 | 0 | 9 | 8 | 10 | 7 |
| IYS.1.2 Data Analysis | 4 | 6 | 7 | 4 | 10 | 6 | 6 | 6 |
| IYS.1.3 Fishery Management, Assessment | 7 | 6 | 10 | 6 | 7 | 1 | 6 | 6 |
| IYS.1.4 Stock Status Assessment | 7 | 4 | 5 | 2 | 6 | 3 | 8 | 5 |
| IYS.1.5 Habitat Assessment | 3 | 4 | 2 | 6 | 4 | 6 | 10 | 5 |
| IYS.1.6 Population identification | 5 | 5 | 5 | 3 | 4 | 5 | 7 | 5 |
| IYS.1.7 Marine Survival, Growth, Migration | 4 | 4 | 5 | 6 | 8 | 10 | 7 | 6 |
| IYS.1.8 Interactions: Wild, Hatchery, Farmed | 1 | 5 | 2 | 6 | 5 | 10 | 10 | 6 |
| IYS.1.9 Toxicology | 0 | 0 | 0 | 5 | 0 | 1 | 2 | 1 |
| IYS.2.1 Freshwater habitats | 5 | 4 | 4 | 8 | 6 | 3 | 4 | 5 |
| IYS.2.2 Marine and Estuarine Habitats | 4 | 2 | 4 | 4 | 7 | 7 | 5 | 5 |
| IYS.2.3 Climate and Ecosystem Models | 1 | 2 | 2 | 4 | 6 | 8 | 2 | 4 |
| IYS.2.4 Adaptation | 2 | 3 | 3 | 3 | 6 | 6 | 2 | 4 |
| IYS.2.5 Policy and Management | 6 | 2 | 5 | 6 | 4 | 2 | 2 | 4 |
| IYS.3.1 Field methods | 5 | 8 | 7 | 3 | 7 | 5 | 8 | 6 |
| IYS.3.2 Individual fish | 3 | 7 | 2 | 0 | 6 | 4 | 5 | 4 |
| IYS.3.3 Fisheries management process | 6 | 3 | 7 | 6 | 5 | 0 | 2 | 4 |
| IYS.3.4 New analyses | 3 | 3 | 2 | 3 | 6 | 4 | 0 | 3 |
| IYS.3.5 Advances genetics, genomics | 3 | 2 | 2 | 3 | 3 | 5 | 3 | 3 |
| IYS.3.6 Science management | 7 | 5 | 3 | 7 | 6 | 5 | 5 | 5 |
| IYS.3.7 Implementation | 7 | 6 | 5 | 6 | 8 | 9 | 5 | 7 |
| IYS.4.1 First Nations Opportunities | 10 | 6 | 9 | 8 | 7 | 4 | 6 | 7 |
| IYS.4.2 Benefits from Salmon | 8 | 3 | 8 | 5 | 4 | 2 | 5 | 5 |
| IYS.4.3 Community engagement | 7 | 6 | 6 | 10 | 6 | 3 | 9 | 7 |
| IYS.4.4 Better science communication | 9 | 5 | 8 | 8 | 9 | 6 | 7 | 7 |
| IYS.4.5 Traditional ecological knowledge | 9 | 4 | 4 | 5 | 4 | 0 | 4 | 4 |
| IYS.4.6 Young scientists | 7 | 4 | 5 | 8 | 4 | 7 | 4 | 6 |
| IYS.4.7 Changing role of salmon in societies | 3 | 2 | 2 | 3 | 2 | 0 | 1 | 2 |
| IYS.5.1 Database Integration | 4 | 6 | 6 | 5 | 7 | 5 | 5 | 5 |
| IYS.5.2 Knowledge management | 5 | 5 | 5 | 8 | 6 | 4 | 4 | 5 |
| IYS.5.3 Data sharing arrangements | 4 | 6 | 6 | 8 | 7 | 4 | 5 | 6 |
| IYS.5.4 Data visualization | 3 | 5 | 3 | 8 | 7 | 5 | 4 | 5 |
| IYS.6.1 International projects | 6 | 2 | 1 | 9 | 6 | 7 | 1 | 5 |
| IYS.6.2 Celebrating success | 7 | 4 | 2 | 10 | 5 | 5 | 7 | 6 |
| IYS.6.3 Outreach methods, awareness | 6 | 4 | 4 | 8 | 4 | 7 | 8 | 6 |
| IYS.6.4 Engagement FM to science to FM | 8 | 4 | 8 | 8 | 7 | 4 | 4 | 6 |
| IYS.6.5 Linking salmon to climate change | 4 | 3 | 4 | 6 | 7 | 6 | 5 | 5 |
Heat Maps by Job Type and Region
We applied a technique for grouping for IYS collaboration topics based on how similar the responses were among topics by people who responded usefully to the survey (a matrix of 37 topics by five choices). The resulting dendrogram shows the hierarchy of groups and is the basis for rearranging the matrix. The frequency of choices in the rearranged matrix is presented as a scale of colours, a heat map, to show the pattern of choices within the resulting groups. This analysis was applied to the survey results partitioned by job types and regions.
Job Type
Because of small sample size, we excluded job type Policy Analyst or Economist with 3 reponders, and DFO region Central and Arctic with 1 responder. Caution might apply to results fro job type Research Scientist (13 responders) and Manager (Staff) (11 responders). The analysis was repeated with choice 1 no,not applicable removed, to emphasize where any collaboration would be valuable, and repeated again after summarizing the 37 choices according to the six IYS themes.
DFO Managers (Staff)
HMjob(y1,"MA","DFO Managers (Staff)","jobCode")
name region jobCode
1 Adam Silverstein Pacific MA
11 Ann Susnik Pacific MA
45 Doug Bliss Gulf MA
60 Helen Kerr Maritimes MA
76 John Holmes Pacific MA
79 Jonathan Fershau Pacific MA
92 Laura Brown Pacific MA
94 Lei Harris Maritimes MA
137 Roger Wysocki HQ MA
138 Ryan Galbraith Pacific MA
146 Serge Doucet Gulf MA
151 Steve Gotch Pacific MA
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 4 2 2 3 1
IYS.1.2 Data Analysis 4 2 3 3 0
IYS.1.3 Fishery Management, Assessment 3 2 3 1 3
IYS.1.4 Stock Status Assessment 2 2 5 2 1
IYS.1.5 Habitat Assessment 4 5 1 2 0
IYS.1.6 Population identification 3 2 6 0 1
IYS.1.7 Marine Survival, Growth, Migration 5 1 4 0 2
IYS.1.8 Interactions: Wild, Hatchery, Farmed 8 2 0 0 2
IYS.1.9 Toxicology 8 2 1 1 0
IYS.2.1 Freshwater habitats 3 4 2 2 1
IYS.2.2 Marine and Estuarine Habitats 4 3 2 2 1
IYS.2.3 Climate and Ecosystem Models 5 5 1 1 0
IYS.2.4 Adaptation 4 6 1 1 0
IYS.2.5 Policy and Management 3 2 2 5 0
IYS.3.1 Field methods 3 2 4 3 0
IYS.3.2 Individual fish 5 4 0 3 0
IYS.3.3 Fisheries management process 3 3 1 4 1
IYS.3.4 New analyses 5 1 4 2 0
IYS.3.5 Advances genetics, genomics 4 2 5 1 0
IYS.3.6 Science management 1 3 4 3 1
IYS.3.7 Implementation 4 1 2 3 2
IYS.4.1 First Nations Opportunities 2 3 0 1 6
IYS.4.2 Benefits from Salmon 2 3 1 3 3
IYS.4.3 Community engagement 4 1 2 3 2
IYS.4.4 Better science communication 2 2 1 5 2
IYS.4.5 Traditional ecological knowledge 2 3 1 2 4
IYS.4.6 Young scientists 3 1 3 5 0
IYS.4.7 Changing role of salmon in societies 5 3 0 4 0
IYS.5.1 Database Integration 4 4 1 2 1
IYS.5.2 Knowledge management 2 4 4 2 0
IYS.5.3 Data sharing arrangements 4 3 2 2 1
IYS.5.4 Data visualization 4 3 4 0 1
IYS.6.1 International projects 4 1 3 2 2
IYS.6.2 Celebrating success 2 2 4 3 1
IYS.6.3 Outreach methods, awareness 2 4 3 2 1
IYS.6.4 Engagement FM to science to FM 3 1 2 3 3
IYS.6.5 Linking salmon to climate change 4 3 2 2 1
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 4.6 2.2 2.8 1.3 1.1
IYS.2 Effects of Changing Habitats 3.8 4.0 1.6 2.2 0.4
IYS.3 New tech and methods 3.6 2.3 2.9 2.7 0.6
IYS.4 Connecting Salmon to People 2.9 2.3 1.1 3.3 2.4
IYS.5 Information Systems 3.5 3.5 2.8 1.5 0.8
IYS.6 Outreach and Communication 3.0 2.2 2.8 2.4 1.6DFO Resource Managers
HMjob(y1,"RM","DFO Resource Managers")
name region jobCode
5 Andrea Goruk Pacific RM
12 Art Demsky Pacific RM
18 Brad Fanos Pacific RM
21 Brittany Jenewein Pacific RM
28 Cathy McClean Pacific RM
43 Diana McHugh Pacific RM
48 Ed Walls Pacific RM
50 Erin Porszt Pacific RM
52 Frederic Butruille Pacific RM
54 Geoff Perry Newfoundland RM
56 Greg Hornby Pacific RM
58 Haakon Hammer Pacific RM
59 Heather Braun Pacific RM
64 Jim Echols Pacific RM
70 Jeff Grout Pacific RM
72 Jeremy Smith Pacific RM
74 Jody Mackenzie-Grieve Pacific RM
78 John Willis Pacific RM
93 Lorne Frisson Pacific RM
96 Les Clint Pacific RM
97 Linda Stevens Pacific RM
111 Matt Mortimer Pacific RM
112 Matthew Townsend Pacific RM
119 Mike Hawkshaw Pacific RM
127 Peter Hall Pacific RM
128 Peter Katinic Pacific RM
130 Reid Schrul Pacific RM
134 Rob Brouwer Pacific RM
139 Sandra Davies Pacific RM
145 Scott Melville Pacific RM
161 Vesta Mather Pacific RM
163 Wilf Luedke Pacific RM
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 7 3 6 7 9
IYS.1.2 Data Analysis 6 5 6 11 4
IYS.1.3 Fishery Management, Assessment 2 4 9 8 9
IYS.1.4 Stock Status Assessment 6 9 8 8 1
IYS.1.5 Habitat Assessment 12 8 7 5 0
IYS.1.6 Population identification 10 7 3 7 5
IYS.1.7 Marine Survival, Growth, Migration 10 4 8 4 6
IYS.1.8 Interactions: Wild, Hatchery, Farmed 12 9 4 3 4
IYS.1.9 Toxicology 14 9 7 2 0
IYS.2.1 Freshwater habitats 10 5 8 6 3
IYS.2.2 Marine and Estuarine Habitats 8 9 8 4 3
IYS.2.3 Climate and Ecosystem Models 9 12 6 3 2
IYS.2.4 Adaptation 9 10 7 3 3
IYS.2.5 Policy and Management 5 13 5 4 5
IYS.3.1 Field methods 6 6 8 6 6
IYS.3.2 Individual fish 13 5 9 3 2
IYS.3.3 Fisheries management process 5 9 5 5 8
IYS.3.4 New analyses 11 9 5 5 2
IYS.3.5 Advances genetics, genomics 11 8 7 4 2
IYS.3.6 Science management 13 5 7 3 4
IYS.3.7 Implementation 7 5 12 6 2
IYS.4.1 First Nations Opportunities 2 7 9 6 8
IYS.4.2 Benefits from Salmon 3 8 9 4 8
IYS.4.3 Community engagement 5 8 8 5 6
IYS.4.4 Better science communication 3 6 8 8 7
IYS.4.5 Traditional ecological knowledge 6 11 9 2 4
IYS.4.6 Young scientists 8 5 11 6 2
IYS.4.7 Changing role of salmon in societies 11 8 8 3 2
IYS.5.1 Database Integration 6 7 9 5 5
IYS.5.2 Knowledge management 8 5 11 3 5
IYS.5.3 Data sharing arrangements 6 6 11 3 6
IYS.5.4 Data visualization 9 8 9 4 2
IYS.6.1 International projects 12 9 7 3 1
IYS.6.2 Celebrating success 11 6 10 4 1
IYS.6.3 Outreach methods, awareness 8 8 8 7 1
IYS.6.4 Engagement FM to science to FM 3 7 8 9 5
IYS.6.5 Linking salmon to climate change 5 12 8 4 3
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 8.8 6.4 6.4 6.1 4.2
IYS.2 Effects of Changing Habitats 8.2 9.8 6.8 4.0 3.2
IYS.3 New tech and methods 9.4 6.7 7.6 4.6 3.7
IYS.4 Connecting Salmon to People 5.4 7.6 8.9 4.9 5.3
IYS.5 Information Systems 7.2 6.5 10.0 3.8 4.5
IYS.6 Outreach and Communication 7.8 8.4 8.2 5.4 2.2DFO Biologists
HMjob(y1,"BI","DFO Biologists")
name region jobCode
4 Alex Levy Maritimes BI
10 Ann-Marie Huang Pacific BI
14 Athena Ogden Pacific BI
22 Bronwyn MacDonald Pacific BI
25 Bruce Patten Pacific BI
35 Dan Selbie Pacific BI
38 David Hardie Maritimes BI
55 Gerald Chaput Gulf BI
75 Joel Harding Pacific BI
82 Karen Dunmall Central and Arctic BI
83 Keith Clarke Newfoundland BI
98 Louise de Mestral Maritimes BI
106 Martha Robertson Newfoundland BI
107 Marthe Berube Quebec BI
109 Mary Thiess Pacific BI
116 Michel Biron Gulf BI
123 Paige Ackerman Pacific BI
124 Patricia Edwards Gulf BI
125 Paul Chamberland Gulf BI
126 Pedro Nilo Quebec BI
129 Philippe Beaulieu Pacific BI
131 Richard Bailey Pacific BI
132 Rob Houtman Pacific BI
141 Sarah Hawkshaw Pacific BI
143 Scott Akenhead Pacific BI
148 Shelee Hamilton Pacific BI
153 Steven Leadbeater Maritimes BI
155 Strahan Tucker Pacific BI
157 Sue Grant Pacific BI
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 5 6 7 5 6
IYS.1.2 Data Analysis 2 8 6 6 7
IYS.1.3 Fishery Management, Assessment 9 3 10 4 3
IYS.1.4 Stock Status Assessment 9 9 4 2 5
IYS.1.5 Habitat Assessment 11 9 7 0 2
IYS.1.6 Population identification 11 7 7 3 1
IYS.1.7 Marine Survival, Growth, Migration 7 4 9 3 6
IYS.1.8 Interactions: Wild, Hatchery, Farmed 9 8 6 5 1
IYS.1.9 Toxicology 24 3 1 1 0
IYS.2.1 Freshwater habitats 6 10 8 3 2
IYS.2.2 Marine and Estuarine Habitats 6 8 10 1 4
IYS.2.3 Climate and Ecosystem Models 7 10 8 2 2
IYS.2.4 Adaptation 7 9 8 3 2
IYS.2.5 Policy and Management 7 15 3 3 1
IYS.3.1 Field methods 8 9 2 4 6
IYS.3.2 Individual fish 10 7 2 6 4
IYS.3.3 Fisheries management process 6 14 4 4 1
IYS.3.4 New analyses 7 11 6 3 2
IYS.3.5 Advances genetics, genomics 12 10 4 2 1
IYS.3.6 Science management 7 10 6 4 2
IYS.3.7 Implementation 3 11 6 7 2
IYS.4.1 First Nations Opportunities 7 6 8 4 4
IYS.4.2 Benefits from Salmon 9 12 5 1 2
IYS.4.3 Community engagement 7 9 6 4 3
IYS.4.4 Better science communication 3 9 7 5 5
IYS.4.5 Traditional ecological knowledge 12 9 4 2 2
IYS.4.6 Young scientists 13 8 4 2 2
IYS.4.7 Changing role of salmon in societies 16 9 1 1 2
IYS.5.1 Database Integration 7 10 5 2 5
IYS.5.2 Knowledge management 9 9 5 3 3
IYS.5.3 Data sharing arrangements 6 11 4 3 5
IYS.5.4 Data visualization 4 12 5 3 5
IYS.6.1 International projects 4 15 6 3 1
IYS.6.2 Celebrating success 7 13 3 4 2
IYS.6.3 Outreach methods, awareness 9 11 6 2 1
IYS.6.4 Engagement FM to science to FM 5 8 10 3 3
IYS.6.5 Linking salmon to climate change 4 15 3 1 6
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 9.7 6.3 6.3 3.2 3.4
IYS.2 Effects of Changing Habitats 6.6 10.4 7.4 2.4 2.2
IYS.3 New tech and methods 7.6 10.3 4.3 4.3 2.6
IYS.4 Connecting Salmon to People 9.6 8.9 5.0 2.7 2.9
IYS.5 Information Systems 6.5 10.5 4.8 2.8 4.5
IYS.6 Outreach and Communication 5.8 12.4 5.6 2.6 2.6DFO Scientists
HMjob(y1,"RE","DFO Scientists")
name region jobCode
27 Carrie Holt Pacific RE
30 Chris McKindsey Quebec RE
37 David Cairns Gulf RE
65 Jim Irvine Pacific RE
89 Kim Hyatt Pacific RE
91 Kristi Miller-Saunders Pacific RE
101 Marc Trudel Maritimes RE
108 Martin Castonguay Quebec RE
115 Michael Scarratt Quebec RE
118 Mike Bradford Pacific RE
135 Bob Devlin Pacific RE
152 Steve MacDonald Pacific RE
154 Stewart Johnson Pacific RE
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 2 3 4 2 2
IYS.1.2 Data Analysis 3 5 0 4 1
IYS.1.3 Fishery Management, Assessment 6 6 0 0 1
IYS.1.4 Stock Status Assessment 6 4 0 3 0
IYS.1.5 Habitat Assessment 3 4 3 1 2
IYS.1.6 Population identification 6 2 1 1 3
IYS.1.7 Marine Survival, Growth, Migration 2 2 3 3 3
IYS.1.8 Interactions: Wild, Hatchery, Farmed 1 3 4 2 3
IYS.1.9 Toxicology 8 3 1 0 1
IYS.2.1 Freshwater habitats 5 5 1 1 1
IYS.2.2 Marine and Estuarine Habitats 2 6 2 0 3
IYS.2.3 Climate and Ecosystem Models 3 3 3 1 3
IYS.2.4 Adaptation 4 4 2 0 3
IYS.2.5 Policy and Management 6 4 2 1 0
IYS.3.1 Field methods 2 6 2 3 0
IYS.3.2 Individual fish 3 7 1 1 1
IYS.3.3 Fisheries management process 8 3 2 0 0
IYS.3.4 New analyses 5 4 2 0 2
IYS.3.5 Advances genetics, genomics 6 3 0 1 3
IYS.3.6 Science management 2 5 4 2 0
IYS.3.7 Implementation 1 3 5 2 2
IYS.4.1 First Nations Opportunities 3 7 1 2 0
IYS.4.2 Benefits from Salmon 4 7 1 1 0
IYS.4.3 Community engagement 3 7 2 1 0
IYS.4.4 Better science communication 2 4 5 1 1
IYS.4.5 Traditional ecological knowledge 7 4 2 0 0
IYS.4.6 Young scientists 2 3 5 2 1
IYS.4.7 Changing role of salmon in societies 7 4 2 0 0
IYS.5.1 Database Integration 5 2 4 1 1
IYS.5.2 Knowledge management 5 4 2 1 1
IYS.5.3 Data sharing arrangements 3 6 3 0 1
IYS.5.4 Data visualization 5 3 2 1 2
IYS.6.1 International projects 2 4 4 1 2
IYS.6.2 Celebrating success 3 4 3 3 0
IYS.6.3 Outreach methods, awareness 2 5 2 2 2
IYS.6.4 Engagement FM to science to FM 4 5 3 0 1
IYS.6.5 Linking salmon to climate change 3 3 4 2 1
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 4.1 3.6 1.8 1.8 1.8
IYS.2 Effects of Changing Habitats 4.0 4.4 2.0 0.6 2.0
IYS.3 New tech and methods 3.9 4.4 2.3 1.3 1.1
IYS.4 Connecting Salmon to People 4.0 5.1 2.6 1.0 0.3
IYS.5 Information Systems 4.5 3.8 2.8 0.8 1.2
IYS.6 Outreach and Communication 2.8 4.2 3.2 1.6 1.2DFO Enhancement Staff
HMjob(y1, "HA","DFO Enhancement Staff")
name region jobCode
2 Al Jonsson Pacific HA
16 Beth Lenentine Maritimes HA
29 Chantal Nessman Pacific HA
34 Dale Desrochers Pacific HA
36 Dave Davies Pacific HA
44 Don MacKinlay Pacific HA
63 James Bell Maritimes HA
66 James Weger Pacific HA
69 Jason Mahoney Pacific HA
87 Kerra Shaw Pacific HA
113 Mike Goguen Maritimes HA
133 Robert Beaumaster Maritimes HA
136 Rob Schaefer Pacific HA
142 Sarah Tuziak Maritimes HA
144 Scott Ducharme Pacific HA
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 0 2 5 2 6
IYS.1.2 Data Analysis 1 4 4 3 3
IYS.1.3 Fishery Management, Assessment 2 2 5 2 4
IYS.1.4 Stock Status Assessment 1 2 5 2 5
IYS.1.5 Habitat Assessment 1 2 1 5 6
IYS.1.6 Population identification 2 2 4 2 5
IYS.1.7 Marine Survival, Growth, Migration 3 1 3 3 5
IYS.1.8 Interactions: Wild, Hatchery, Farmed 2 2 1 1 9
IYS.1.9 Toxicology 3 5 4 1 2
IYS.2.1 Freshwater habitats 2 5 3 2 3
IYS.2.2 Marine and Estuarine Habitats 1 6 3 2 3
IYS.2.3 Climate and Ecosystem Models 3 5 4 2 1
IYS.2.4 Adaptation 2 7 2 2 2
IYS.2.5 Policy and Management 3 7 2 0 3
IYS.3.1 Field methods 0 3 6 2 4
IYS.3.2 Individual fish 2 3 6 1 3
IYS.3.3 Fisheries management process 3 4 5 1 2
IYS.3.4 New analyses 4 6 3 1 1
IYS.3.5 Advances genetics, genomics 3 3 6 1 2
IYS.3.6 Science management 3 2 5 2 3
IYS.3.7 Implementation 3 2 4 2 4
IYS.4.1 First Nations Opportunities 1 3 6 2 3
IYS.4.2 Benefits from Salmon 1 5 4 2 3
IYS.4.3 Community engagement 0 3 4 2 6
IYS.4.4 Better science communication 1 5 2 2 5
IYS.4.5 Traditional ecological knowledge 1 5 5 3 1
IYS.4.6 Young scientists 1 5 6 0 3
IYS.4.7 Changing role of salmon in societies 3 7 2 1 2
IYS.5.1 Database Integration 1 4 6 0 4
IYS.5.2 Knowledge management 1 5 6 1 2
IYS.5.3 Data sharing arrangements 0 5 6 1 3
IYS.5.4 Data visualization 2 4 6 0 3
IYS.6.1 International projects 2 7 4 1 1
IYS.6.2 Celebrating success 0 5 3 3 4
IYS.6.3 Outreach methods, awareness 0 4 4 2 5
IYS.6.4 Engagement FM to science to FM 3 3 4 3 2
IYS.6.5 Linking salmon to climate change 1 5 4 2 3
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 1.7 2.4 3.6 2.3 5.0
IYS.2 Effects of Changing Habitats 2.2 6.0 2.8 1.6 2.4
IYS.3 New tech and methods 2.6 3.3 5.0 1.4 2.7
IYS.4 Connecting Salmon to People 1.1 4.7 4.1 1.7 3.3
IYS.5 Information Systems 1.0 4.5 6.0 0.5 3.0
IYS.6 Outreach and Communication 1.2 4.8 3.8 2.2 3.0Interpretation: Collaboration Potential by Job Type
The clearest choices regarding collaboration were:
* disinterest in toxicology and in the role of salmon in societies
* interest but inability to collaborate, on linking salmon to climate change, including adaptation of salmon, and policy and management.
* existing activities need assistance with implmentation of existing/new science, with various aspects of data management including visualization, sharing, integration (IYS theme 5).
* suprisingly, the need collabortion with data managment did not have corresponding offers of knowledge, but there was interest in helping with data analysis and the implementation of science.
* The outstanding choice for as “critical to my work and should be a DFO priority” was field data, followed (all of equal importance) by First Nations opportunties, fisheries management and assessment, marine growth and survival, better science communication, and better field methods.
Tentative Summary
DFO staff have a problem with data collection and management and this is likely blocking offers help with data analysis and improve management technology. Combined interest in stock assessment methodology with marine survival points to the fundamental problem of predictability for fisheries. Could it be that a lot of DFO staff know exactly what needs to be fixed, but cannot marshall the projects/programs necessary to accomplish that fix?
The impression I received from all of the responses to IYS topics was a call for modernization of the year to year business of fisheries managment in DFO. Better tools, sort of: more data more easily, better and more accessible methods for archiving, assembling and applying data. The route to accomplish this reflects the goal of the International Year of the Salmon (paraphrased): radically efficient collaboration across technical staff, biologists, scientists, and fishery managers has produced a quantum leap in the application of new and existing science.
By DFO Regions
Because of small sample sizes, we excluded DFO regions Central and Arctic with 1 responder, National Capital Region with 2, and Newfoundland and Labrador with 3. That leaves 4 regions: Maritimes with 16, Gulf with 7, Quebec with 5, and Pacific with 90.
Maritimes Region
table(y1$region)
# region codes, survey1: Pacific, Maritimes, Quebec, Gulf, Newfoundland, HQ, Central and Arctic.
HMjob(y1, "Maritimes","Maritimes Region","region")
Central and Arctic Gulf HQ
1 7 2
Maritimes Newfoundland Pacific
16 3 90
Quebec
5
name region jobCode
4 Alex Levy Maritimes BI
15 Becky Graham Maritimes EG
16 Beth Lenentine Maritimes HA
33 Cynthia Hawthorne Maritimes EG
38 David Hardie Maritimes BI
60 Helen Kerr Maritimes MA
63 James Bell Maritimes HA
94 Lei Harris Maritimes MA
95 Leroy Anderson Maritimes EG
98 Louise de Mestral Maritimes BI
101 Marc Trudel Maritimes RE
113 Mike Goguen Maritimes HA
120 Mike Thorburne Maritimes EG
133 Robert Beaumaster Maritimes HA
142 Sarah Tuziak Maritimes HA
153 Steven Leadbeater Maritimes BI
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 1 3 4 2 6
IYS.1.2 Data Analysis 3 5 1 4 3
IYS.1.3 Fishery Management, Assessment 4 2 3 2 5
IYS.1.4 Stock Status Assessment 4 3 4 2 3
IYS.1.5 Habitat Assessment 3 5 3 2 3
IYS.1.6 Population identification 2 3 4 3 4
IYS.1.7 Marine Survival, Growth, Migration 1 1 6 2 6
IYS.1.8 Interactions: Wild, Hatchery, Farmed 1 2 4 4 5
IYS.1.9 Toxicology 7 4 3 1 1
IYS.2.1 Freshwater habitats 1 10 2 1 2
IYS.2.2 Marine and Estuarine Habitats 1 9 2 1 3
IYS.2.3 Climate and Ecosystem Models 5 6 3 0 2
IYS.2.4 Adaptation 2 7 3 2 2
IYS.2.5 Policy and Management 5 6 2 2 1
IYS.3.1 Field methods 2 1 6 2 5
IYS.3.2 Individual fish 1 2 6 5 2
IYS.3.3 Fisheries management process 4 3 6 1 2
IYS.3.4 New analyses 3 7 3 0 3
IYS.3.5 Advances genetics, genomics 3 5 5 0 3
IYS.3.6 Science management 0 6 7 1 2
IYS.3.7 Implementation 1 2 7 1 5
IYS.4.1 First Nations Opportunities 2 4 2 4 4
IYS.4.2 Benefits from Salmon 3 5 3 2 3
IYS.4.3 Community engagement 2 6 3 2 3
IYS.4.4 Better science communication 2 5 3 3 3
IYS.4.5 Traditional ecological knowledge 5 3 2 4 2
IYS.4.6 Young scientists 3 5 3 2 3
IYS.4.7 Changing role of salmon in societies 6 4 3 2 1
IYS.5.1 Database Integration 3 4 5 2 2
IYS.5.2 Knowledge management 4 5 3 2 2
IYS.5.3 Data sharing arrangements 2 6 3 3 2
IYS.5.4 Data visualization 4 5 3 2 2
IYS.6.1 International projects 2 8 3 1 2
IYS.6.2 Celebrating success 3 4 3 5 1
IYS.6.3 Outreach methods, awareness 1 7 4 3 1
IYS.6.4 Engagement FM to science to FM 2 8 2 3 1
IYS.6.5 Linking salmon to climate change 1 9 2 2 2
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 2.9 3.1 3.6 2.4 4.0
IYS.2 Effects of Changing Habitats 2.8 7.6 2.4 1.2 2.0
IYS.3 New tech and methods 2.0 3.7 5.7 1.4 3.1
IYS.4 Connecting Salmon to People 3.3 4.6 2.7 2.7 2.7
IYS.5 Information Systems 3.2 5.0 3.5 2.2 2.0
IYS.6 Outreach and Communication 1.8 7.2 2.8 2.8 1.4Gulf Region
HMjob(y1, "Gulf","Gulf Region","region" )
name region jobCode
37 David Cairns Gulf RE
45 Doug Bliss Gulf MA
55 Gerald Chaput Gulf BI
116 Michel Biron Gulf BI
124 Patricia Edwards Gulf BI
125 Paul Chamberland Gulf BI
146 Serge Doucet Gulf MA
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 0 2 2 3 0
IYS.1.2 Data Analysis 0 2 2 2 1
IYS.1.3 Fishery Management, Assessment 1 2 2 0 2
IYS.1.4 Stock Status Assessment 1 3 1 0 2
IYS.1.5 Habitat Assessment 1 4 1 1 0
IYS.1.6 Population identification 2 1 2 1 1
IYS.1.7 Marine Survival, Growth, Migration 2 1 2 1 1
IYS.1.8 Interactions: Wild, Hatchery, Farmed 3 3 0 0 1
IYS.1.9 Toxicology 5 0 1 1 0
IYS.2.1 Freshwater habitats 0 3 2 2 0
IYS.2.2 Marine and Estuarine Habitats 1 2 3 1 0
IYS.2.3 Climate and Ecosystem Models 2 1 2 2 0
IYS.2.4 Adaptation 2 2 1 2 0
IYS.2.5 Policy and Management 2 3 0 2 0
IYS.3.1 Field methods 0 2 3 2 0
IYS.3.2 Individual fish 3 1 1 2 0
IYS.3.3 Fisheries management process 3 2 0 2 0
IYS.3.4 New analyses 2 2 2 1 0
IYS.3.5 Advances genetics, genomics 2 1 2 2 0
IYS.3.6 Science management 3 0 1 1 2
IYS.3.7 Implementation 1 2 1 3 0
IYS.4.1 First Nations Opportunities 1 1 2 0 3
IYS.4.2 Benefits from Salmon 1 1 2 1 2
IYS.4.3 Community engagement 1 2 1 1 2
IYS.4.4 Better science communication 1 1 2 0 3
IYS.4.5 Traditional ecological knowledge 2 2 0 1 2
IYS.4.6 Young scientists 2 2 1 1 1
IYS.4.7 Changing role of salmon in societies 2 2 0 2 1
IYS.5.1 Database Integration 1 2 3 1 0
IYS.5.2 Knowledge management 1 2 1 2 1
IYS.5.3 Data sharing arrangements 1 2 4 0 0
IYS.5.4 Data visualization 1 2 4 0 0
IYS.6.1 International projects 1 3 0 1 2
IYS.6.2 Celebrating success 1 3 0 1 2
IYS.6.3 Outreach methods, awareness 1 1 3 1 1
IYS.6.4 Engagement FM to science to FM 1 1 2 1 2
IYS.6.5 Linking salmon to climate change 1 2 2 1 1
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 1.7 2.0 1.4 1.0 0.9
IYS.2 Effects of Changing Habitats 1.4 2.2 1.6 1.8 0.0
IYS.3 New tech and methods 2.0 1.4 1.4 1.9 0.3
IYS.4 Connecting Salmon to People 1.4 1.6 1.1 0.9 2.0
IYS.5 Information Systems 1.0 2.0 3.0 0.8 0.2
IYS.6 Outreach and Communication 1.0 2.0 1.4 1.0 1.6Quebec Region
HMjob(y1, "Quebec","Quebec Region","region")
name region jobCode
30 Chris McKindsey Quebec RE
107 Marthe Berube Quebec BI
108 Martin Castonguay Quebec RE
115 Michael Scarratt Quebec RE
126 Pedro Nilo Quebec BI
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 1 1 1 1 1
IYS.1.2 Data Analysis 1 1 1 2 0
IYS.1.3 Fishery Management, Assessment 3 1 1 0 0
IYS.1.4 Stock Status Assessment 1 3 1 0 0
IYS.1.5 Habitat Assessment 1 2 1 0 1
IYS.1.6 Population identification 3 2 0 0 0
IYS.1.7 Marine Survival, Growth, Migration 0 2 1 1 1
IYS.1.8 Interactions: Wild, Hatchery, Farmed 0 3 2 0 0
IYS.1.9 Toxicology 2 3 0 0 0
IYS.2.1 Freshwater habitats 3 0 1 0 1
IYS.2.2 Marine and Estuarine Habitats 1 2 1 0 1
IYS.2.3 Climate and Ecosystem Models 2 0 2 0 1
IYS.2.4 Adaptation 2 1 1 0 1
IYS.2.5 Policy and Management 2 2 1 0 0
IYS.3.1 Field methods 1 2 0 1 1
IYS.3.2 Individual fish 1 3 0 0 1
IYS.3.3 Fisheries management process 2 3 0 0 0
IYS.3.4 New analyses 3 1 0 1 0
IYS.3.5 Advances genetics, genomics 4 1 0 0 0
IYS.3.6 Science management 0 4 1 0 0
IYS.3.7 Implementation 0 3 1 1 0
IYS.4.1 First Nations Opportunities 1 2 1 0 1
IYS.4.2 Benefits from Salmon 1 3 1 0 0
IYS.4.3 Community engagement 1 2 1 1 0
IYS.4.4 Better science communication 1 1 2 1 0
IYS.4.5 Traditional ecological knowledge 2 1 1 1 0
IYS.4.6 Young scientists 2 2 0 1 0
IYS.4.7 Changing role of salmon in societies 2 3 0 0 0
IYS.5.1 Database Integration 2 1 1 0 1
IYS.5.2 Knowledge management 3 2 0 0 0
IYS.5.3 Data sharing arrangements 1 2 1 0 1
IYS.5.4 Data visualization 2 1 1 0 1
IYS.6.1 International projects 1 2 1 1 0
IYS.6.2 Celebrating success 2 2 0 1 0
IYS.6.3 Outreach methods, awareness 1 3 0 1 0
IYS.6.4 Engagement FM to science to FM 1 3 1 0 0
IYS.6.5 Linking salmon to climate change 3 2 0 0 0
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 1.3 2.0 0.9 0.4 0.3
IYS.2 Effects of Changing Habitats 2.0 1.0 1.2 0.0 0.8
IYS.3 New tech and methods 1.6 2.4 0.3 0.4 0.3
IYS.4 Connecting Salmon to People 1.4 2.0 0.9 0.6 0.1
IYS.5 Information Systems 2.0 1.5 0.8 0.0 0.8
IYS.6 Outreach and Communication 1.6 2.4 0.4 0.6 0.0Pacific Region
HMjob(y1, "Pacific","Pacific Region","region")
name region jobCode
1 Adam Silverstein Pacific MA
2 Al Jonsson Pacific HA
3 Aleta Rushton Pacific EG
5 Andrea Goruk Pacific RM
6 Andrew Campbell Pacific EG
9 Angela Stadel Pacific PO
10 Ann-Marie Huang Pacific BI
11 Ann Susnik Pacific MA
12 Art Demsky Pacific RM
14 Athena Ogden Pacific BI
18 Brad Fanos Pacific RM
20 Brian Leaf Pacific EG
21 Brittany Jenewein Pacific RM
22 Bronwyn MacDonald Pacific BI
24 Bruce Baxter Pacific EG
25 Bruce Patten Pacific BI
27 Carrie Holt Pacific RE
28 Cathy McClean Pacific RM
29 Chantal Nessman Pacific HA
32 Colin Nettles Pacific EG
34 Dale Desrochers Pacific HA
35 Dan Selbie Pacific BI
36 Dave Davies Pacific HA
43 Diana McHugh Pacific RM
44 Don MacKinlay Pacific HA
46 Eamon Miyagi Pacific EG
48 Ed Walls Pacific RM
49 Elan Park Pacific PO
50 Erin Porszt Pacific RM
52 Frederic Butruille Pacific RM
56 Greg Hornby Pacific RM
58 Haakon Hammer Pacific RM
59 Heather Braun Pacific RM
64 Jim Echols Pacific RM
65 Jim Irvine Pacific RE
66 James Weger Pacific HA
67 Jason Evans Pacific EG
69 Jason Mahoney Pacific HA
70 Jeff Grout Pacific RM
72 Jeremy Smith Pacific RM
74 Jody Mackenzie-Grieve Pacific RM
75 Joel Harding Pacific BI
76 John Holmes Pacific MA
78 John Willis Pacific RM
79 Jonathan Fershau Pacific MA
80 Julia Bradshaw Pacific EG
87 Kerra Shaw Pacific HA
89 Kim Hyatt Pacific RE
91 Kristi Miller-Saunders Pacific RE
92 Laura Brown Pacific MA
93 Lorne Frisson Pacific RM
96 Les Clint Pacific RM
97 Linda Stevens Pacific RM
103 Marilyn Helin Pacific EG
104 Marina Milligan Pacific EG
109 Mary Thiess Pacific BI
111 Matt Mortimer Pacific RM
112 Matthew Townsend Pacific RM
118 Mike Bradford Pacific RE
119 Mike Hawkshaw Pacific RM
122 Nicholas Komick Pacific EG
123 Paige Ackerman Pacific BI
127 Peter Hall Pacific RM
128 Peter Katinic Pacific RM
129 Philippe Beaulieu Pacific BI
130 Reid Schrul Pacific RM
131 Richard Bailey Pacific BI
132 Rob Houtman Pacific BI
134 Rob Brouwer Pacific RM
135 Bob Devlin Pacific RE
136 Rob Schaefer Pacific HA
138 Ryan Galbraith Pacific MA
139 Sandra Davies Pacific RM
140 Sandy Devcic Pacific EG
141 Sarah Hawkshaw Pacific BI
143 Scott Akenhead Pacific BI
144 Scott Ducharme Pacific HA
145 Scott Melville Pacific RM
147 Shaun Spenard Pacific EG
148 Shelee Hamilton Pacific BI
150 Stefan Howarth Pacific EG
151 Steve Gotch Pacific MA
152 Steve MacDonald Pacific RE
154 Stewart Johnson Pacific RE
155 Strahan Tucker Pacific BI
156 Stuart LePage Pacific EG
157 Sue Grant Pacific BI
159 Tracy Cone Pacific EG
161 Vesta Mather Pacific RM
163 Wilf Luedke Pacific RM
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 17 11 21 16 25
IYS.1.2 Data Analysis 15 19 19 21 16
IYS.1.3 Fishery Management, Assessment 15 17 24 16 18
IYS.1.4 Stock Status Assessment 22 20 21 18 9
IYS.1.5 Habitat Assessment 28 22 17 13 10
IYS.1.6 Population identification 28 18 21 12 11
IYS.1.7 Marine Survival, Growth, Migration 28 10 22 13 17
IYS.1.8 Interactions: Wild, Hatchery, Farmed 29 19 17 9 16
IYS.1.9 Toxicology 47 21 16 3 3
IYS.2.1 Freshwater habitats 24 21 23 11 11
IYS.2.2 Marine and Estuarine Habitats 24 24 20 10 12
IYS.2.3 Climate and Ecosystem Models 23 30 22 9 6
IYS.2.4 Adaptation 26 32 16 7 9
IYS.2.5 Policy and Management 21 32 18 10 9
IYS.3.1 Field methods 17 24 17 16 16
IYS.3.2 Individual fish 28 22 18 13 9
IYS.3.3 Fisheries management process 24 26 16 12 12
IYS.3.4 New analyses 27 26 20 11 6
IYS.3.5 Advances genetics, genomics 35 19 20 10 6
IYS.3.6 Science management 28 17 21 14 10
IYS.3.7 Implementation 20 14 26 20 10
IYS.4.1 First Nations Opportunities 13 20 26 13 18
IYS.4.2 Benefits from Salmon 18 30 19 9 14
IYS.4.3 Community engagement 17 22 21 13 17
IYS.4.4 Better science communication 10 22 21 19 18
IYS.4.5 Traditional ecological knowledge 20 33 22 6 9
IYS.4.6 Young scientists 23 17 30 13 7
IYS.4.7 Changing role of salmon in societies 38 25 13 8 6
IYS.5.1 Database Integration 16 23 24 12 15
IYS.5.2 Knowledge management 15 23 31 11 10
IYS.5.3 Data sharing arrangements 14 24 24 12 16
IYS.5.4 Data visualization 18 23 27 10 12
IYS.6.1 International projects 25 28 25 6 6
IYS.6.2 Celebrating success 21 26 21 11 11
IYS.6.3 Outreach methods, awareness 22 24 20 12 12
IYS.6.4 Engagement FM to science to FM 16 19 25 14 16
IYS.6.5 Linking salmon to climate change 16 31 19 10 14
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 25.4 17.4 19.8 13.4 13.9
IYS.2 Effects of Changing Habitats 23.6 27.8 19.8 9.4 9.4
IYS.3 New tech and methods 25.6 21.1 19.7 13.7 9.9
IYS.4 Connecting Salmon to People 19.9 24.1 21.7 11.6 12.7
IYS.5 Information Systems 15.8 23.2 26.5 11.2 13.2
IYS.6 Outreach and Communication 20.0 25.6 22.0 10.6 11.8Within in the Pacific Region
Pacific Region of DFO had 90 of the 124 useful responses, allowing a within-region analysis of collaboration choices for IYS topics by job type. The exception is Policy and Economists, with only two responders from Pacific (Angela Stadel, Elan Parl).
Four DFO regions, namely Newfoundland and Labrador, Quebec, Gulf, and Maritimes deal with Atlantic Salmon instead of Pacific Salmon. They had 31 useful responses, including zero for job type PO.
y1p=y1[y1$region == "Pacific",]
y1a=y1[ !(y1$region %in% c("Pacific","Central and Arctic","HQ")) ,] # 31
table(y1p$jobCode) %>% kable(caption = "Pacific", col.names=c("Job Type", "Frequency"))
table(y1a$jobCode) %>% kable(caption = "Atlantic", col.names=c("Job Type", "Frequency"))| Job Type | Frequency |
|---|---|
| BI | 16 |
| EG | 16 |
| HA | 10 |
| MA | 7 |
| PO | 2 |
| RE | 8 |
| RM | 31 |
| Job Type | Frequency |
|---|---|
| BI | 12 |
| EG | 4 |
| HA | 5 |
| MA | 4 |
| RE | 5 |
| RM | 1 |
| **Table x.* | * Frequency of Job Type within Pacific Region, from 90 useful survey responses. |
DFO Pacific Managers (Staff)
HMjob(y1p,"MA","DFO Pacific: Managers (Staff)")
name region jobCode
1 Adam Silverstein Pacific MA
11 Ann Susnik Pacific MA
76 John Holmes Pacific MA
79 Jonathan Fershau Pacific MA
92 Laura Brown Pacific MA
138 Ryan Galbraith Pacific MA
151 Steve Gotch Pacific MA
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 2 1 1 2 1
IYS.1.2 Data Analysis 3 1 1 2 0
IYS.1.3 Fishery Management, Assessment 2 2 2 0 1
IYS.1.4 Stock Status Assessment 1 2 3 1 0
IYS.1.5 Habitat Assessment 2 3 1 1 0
IYS.1.6 Population identification 1 2 4 0 0
IYS.1.7 Marine Survival, Growth, Migration 3 1 2 0 1
IYS.1.8 Interactions: Wild, Hatchery, Farmed 5 1 0 0 1
IYS.1.9 Toxicology 5 2 0 0 0
IYS.2.1 Freshwater habitats 2 2 1 1 1
IYS.2.2 Marine and Estuarine Habitats 3 1 1 1 1
IYS.2.3 Climate and Ecosystem Models 3 4 0 0 0
IYS.2.4 Adaptation 3 4 0 0 0
IYS.2.5 Policy and Management 2 2 2 1 0
IYS.3.1 Field methods 2 1 2 2 0
IYS.3.2 Individual fish 3 2 0 2 0
IYS.3.3 Fisheries management process 2 3 1 1 0
IYS.3.4 New analyses 4 0 3 0 0
IYS.3.5 Advances genetics, genomics 3 0 4 0 0
IYS.3.6 Science management 1 3 3 0 0
IYS.3.7 Implementation 3 1 2 0 1
IYS.4.1 First Nations Opportunities 1 2 0 1 3
IYS.4.2 Benefits from Salmon 1 2 1 2 1
IYS.4.3 Community engagement 3 0 2 2 0
IYS.4.4 Better science communication 1 2 1 3 0
IYS.4.5 Traditional ecological knowledge 1 2 1 0 3
IYS.4.6 Young scientists 2 1 1 3 0
IYS.4.7 Changing role of salmon in societies 4 2 0 1 0
IYS.5.1 Database Integration 3 2 0 1 1
IYS.5.2 Knowledge management 1 2 4 0 0
IYS.5.3 Data sharing arrangements 3 1 0 2 1
IYS.5.4 Data visualization 3 1 2 0 1
IYS.6.1 International projects 3 0 3 0 1
IYS.6.2 Celebrating success 1 1 3 2 0
IYS.6.3 Outreach methods, awareness 2 2 1 1 1
IYS.6.4 Engagement FM to science to FM 2 1 2 0 2
IYS.6.5 Linking salmon to climate change 3 1 1 1 1
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 2.7 1.7 1.6 0.7 0.4
IYS.2 Effects of Changing Habitats 2.6 2.6 0.8 0.6 0.4
IYS.3 New tech and methods 2.6 1.4 2.1 0.7 0.1
IYS.4 Connecting Salmon to People 1.9 1.6 0.9 1.7 1.0
IYS.5 Information Systems 2.5 1.5 1.5 0.8 0.8
IYS.6 Outreach and Communication 2.2 1.0 2.0 0.8 1.0DFO Resource Managers
HMjob(y1p,"RM","DFO Pacific: Resource Managers")
name region jobCode
5 Andrea Goruk Pacific RM
12 Art Demsky Pacific RM
18 Brad Fanos Pacific RM
21 Brittany Jenewein Pacific RM
28 Cathy McClean Pacific RM
43 Diana McHugh Pacific RM
48 Ed Walls Pacific RM
50 Erin Porszt Pacific RM
52 Frederic Butruille Pacific RM
56 Greg Hornby Pacific RM
58 Haakon Hammer Pacific RM
59 Heather Braun Pacific RM
64 Jim Echols Pacific RM
70 Jeff Grout Pacific RM
72 Jeremy Smith Pacific RM
74 Jody Mackenzie-Grieve Pacific RM
78 John Willis Pacific RM
93 Lorne Frisson Pacific RM
96 Les Clint Pacific RM
97 Linda Stevens Pacific RM
111 Matt Mortimer Pacific RM
112 Matthew Townsend Pacific RM
119 Mike Hawkshaw Pacific RM
127 Peter Hall Pacific RM
128 Peter Katinic Pacific RM
130 Reid Schrul Pacific RM
134 Rob Brouwer Pacific RM
139 Sandra Davies Pacific RM
145 Scott Melville Pacific RM
161 Vesta Mather Pacific RM
163 Wilf Luedke Pacific RM
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 7 2 6 7 9
IYS.1.2 Data Analysis 6 5 6 10 4
IYS.1.3 Fishery Management, Assessment 2 3 9 8 9
IYS.1.4 Stock Status Assessment 5 9 8 8 1
IYS.1.5 Habitat Assessment 11 8 7 5 0
IYS.1.6 Population identification 10 7 3 7 4
IYS.1.7 Marine Survival, Growth, Migration 9 4 8 4 6
IYS.1.8 Interactions: Wild, Hatchery, Farmed 12 9 4 3 3
IYS.1.9 Toxicology 13 9 7 2 0
IYS.2.1 Freshwater habitats 9 5 8 6 3
IYS.2.2 Marine and Estuarine Habitats 7 9 8 4 3
IYS.2.3 Climate and Ecosystem Models 8 12 6 3 2
IYS.2.4 Adaptation 9 10 7 2 3
IYS.2.5 Policy and Management 4 13 5 4 5
IYS.3.1 Field methods 5 6 8 6 6
IYS.3.2 Individual fish 12 5 9 3 2
IYS.3.3 Fisheries management process 5 9 4 5 8
IYS.3.4 New analyses 10 9 5 5 2
IYS.3.5 Advances genetics, genomics 11 8 6 4 2
IYS.3.6 Science management 13 4 7 3 4
IYS.3.7 Implementation 7 5 11 6 2
IYS.4.1 First Nations Opportunities 1 7 9 6 8
IYS.4.2 Benefits from Salmon 2 8 9 4 8
IYS.4.3 Community engagement 4 8 8 5 6
IYS.4.4 Better science communication 3 5 8 8 7
IYS.4.5 Traditional ecological knowledge 5 11 9 2 4
IYS.4.6 Young scientists 7 5 11 6 2
IYS.4.7 Changing role of salmon in societies 10 8 8 3 2
IYS.5.1 Database Integration 6 7 8 5 5
IYS.5.2 Knowledge management 8 5 10 3 5
IYS.5.3 Data sharing arrangements 6 6 10 3 6
IYS.5.4 Data visualization 8 8 9 4 2
IYS.6.1 International projects 11 9 7 3 1
IYS.6.2 Celebrating success 10 6 10 4 1
IYS.6.3 Outreach methods, awareness 7 8 8 7 1
IYS.6.4 Engagement FM to science to FM 2 7 8 9 5
IYS.6.5 Linking salmon to climate change 4 12 8 4 3
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 8.3 6.2 6.4 6.0 4.0
IYS.2 Effects of Changing Habitats 7.4 9.8 6.8 3.8 3.2
IYS.3 New tech and methods 9.0 6.6 7.1 4.6 3.7
IYS.4 Connecting Salmon to People 4.6 7.4 8.9 4.9 5.3
IYS.5 Information Systems 7.0 6.5 9.2 3.8 4.5
IYS.6 Outreach and Communication 6.8 8.4 8.2 5.4 2.2DFO Biologists
HMjob(y1p,"BI","DFO Pacific: Biologists")
name region jobCode
10 Ann-Marie Huang Pacific BI
14 Athena Ogden Pacific BI
22 Bronwyn MacDonald Pacific BI
25 Bruce Patten Pacific BI
35 Dan Selbie Pacific BI
75 Joel Harding Pacific BI
109 Mary Thiess Pacific BI
123 Paige Ackerman Pacific BI
129 Philippe Beaulieu Pacific BI
131 Richard Bailey Pacific BI
132 Rob Houtman Pacific BI
141 Sarah Hawkshaw Pacific BI
143 Scott Akenhead Pacific BI
148 Shelee Hamilton Pacific BI
155 Strahan Tucker Pacific BI
157 Sue Grant Pacific BI
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 5 4 2 2 3
IYS.1.2 Data Analysis 1 5 1 4 5
IYS.1.3 Fishery Management, Assessment 4 1 5 4 2
IYS.1.4 Stock Status Assessment 5 3 2 2 4
IYS.1.5 Habitat Assessment 8 3 4 0 1
IYS.1.6 Population identification 9 2 4 1 0
IYS.1.7 Marine Survival, Growth, Migration 6 1 3 2 4
IYS.1.8 Interactions: Wild, Hatchery, Farmed 6 4 4 1 1
IYS.1.9 Toxicology 14 1 1 0 0
IYS.2.1 Freshwater habitats 6 4 5 0 1
IYS.2.2 Marine and Estuarine Habitats 5 4 4 1 2
IYS.2.3 Climate and Ecosystem Models 5 4 5 1 1
IYS.2.4 Adaptation 6 6 2 1 1
IYS.2.5 Policy and Management 4 8 1 2 1
IYS.3.1 Field methods 7 5 1 1 2
IYS.3.2 Individual fish 8 4 1 1 2
IYS.3.3 Fisheries management process 3 6 3 3 1
IYS.3.4 New analyses 3 7 3 2 1
IYS.3.5 Advances genetics, genomics 9 4 2 1 0
IYS.3.6 Science management 5 4 3 3 1
IYS.3.7 Implementation 3 4 4 4 1
IYS.4.1 First Nations Opportunities 6 3 5 1 1
IYS.4.2 Benefits from Salmon 7 8 0 0 1
IYS.4.3 Community engagement 6 5 3 1 1
IYS.4.4 Better science communication 2 6 3 3 2
IYS.4.5 Traditional ecological knowledge 7 6 3 0 0
IYS.4.6 Young scientists 9 3 3 0 1
IYS.4.7 Changing role of salmon in societies 10 4 0 1 1
IYS.5.1 Database Integration 4 5 2 1 4
IYS.5.2 Knowledge management 3 5 4 2 2
IYS.5.3 Data sharing arrangements 3 5 2 2 4
IYS.5.4 Data visualization 2 6 2 2 4
IYS.6.1 International projects 3 9 4 0 0
IYS.6.2 Celebrating success 6 8 1 0 1
IYS.6.3 Outreach methods, awareness 8 6 2 0 0
IYS.6.4 Engagement FM to science to FM 4 3 6 1 2
IYS.6.5 Linking salmon to climate change 3 8 1 0 4
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 6.4 2.7 2.9 1.8 2.2
IYS.2 Effects of Changing Habitats 5.2 5.2 3.4 1.0 1.2
IYS.3 New tech and methods 5.4 4.9 2.4 2.1 1.1
IYS.4 Connecting Salmon to People 6.7 5.0 2.4 0.9 1.0
IYS.5 Information Systems 3.0 5.2 2.5 1.8 3.5
IYS.6 Outreach and Communication 4.8 6.8 2.8 0.2 1.4DFO Scientists
HMjob(y1p,"RE","DFO Pacific: Scientists")
name region jobCode
27 Carrie Holt Pacific RE
65 Jim Irvine Pacific RE
89 Kim Hyatt Pacific RE
91 Kristi Miller-Saunders Pacific RE
118 Mike Bradford Pacific RE
135 Bob Devlin Pacific RE
152 Steve MacDonald Pacific RE
154 Stewart Johnson Pacific RE
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 1 1 4 1 1
IYS.1.2 Data Analysis 2 3 0 2 1
IYS.1.3 Fishery Management, Assessment 2 5 0 0 1
IYS.1.4 Stock Status Assessment 4 1 0 3 0
IYS.1.5 Habitat Assessment 2 1 2 1 2
IYS.1.6 Population identification 2 2 1 1 2
IYS.1.7 Marine Survival, Growth, Migration 1 1 2 2 2
IYS.1.8 Interactions: Wild, Hatchery, Farmed 0 1 3 2 2
IYS.1.9 Toxicology 5 1 1 0 1
IYS.2.1 Freshwater habitats 2 3 1 1 1
IYS.2.2 Marine and Estuarine Habitats 1 3 2 0 2
IYS.2.3 Climate and Ecosystem Models 0 3 2 1 2
IYS.2.4 Adaptation 1 3 2 0 2
IYS.2.5 Policy and Management 2 3 2 1 0
IYS.3.1 Field methods 1 5 0 2 0
IYS.3.2 Individual fish 1 5 0 1 1
IYS.3.3 Fisheries management process 4 2 2 0 0
IYS.3.4 New analyses 2 3 2 0 1
IYS.3.5 Advances genetics, genomics 2 3 0 1 2
IYS.3.6 Science management 1 2 3 2 0
IYS.3.7 Implementation 0 1 4 2 1
IYS.4.1 First Nations Opportunities 1 4 1 2 0
IYS.4.2 Benefits from Salmon 2 4 1 1 0
IYS.4.3 Community engagement 1 4 2 1 0
IYS.4.4 Better science communication 0 2 4 1 1
IYS.4.5 Traditional ecological knowledge 3 3 2 0 0
IYS.4.6 Young scientists 0 1 5 2 0
IYS.4.7 Changing role of salmon in societies 4 2 2 0 0
IYS.5.1 Database Integration 2 2 3 1 0
IYS.5.2 Knowledge management 2 3 2 1 0
IYS.5.3 Data sharing arrangements 2 4 2 0 0
IYS.5.4 Data visualization 3 2 1 1 1
IYS.6.1 International projects 1 2 3 1 1
IYS.6.2 Celebrating success 0 3 3 2 0
IYS.6.3 Outreach methods, awareness 0 3 2 2 1
IYS.6.4 Engagement FM to science to FM 1 3 3 0 1
IYS.6.5 Linking salmon to climate change 0 2 4 2 0
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 2.1 1.8 1.4 1.3 1.3
IYS.2 Effects of Changing Habitats 1.2 3.0 1.8 0.6 1.4
IYS.3 New tech and methods 1.6 3.0 1.6 1.1 0.7
IYS.4 Connecting Salmon to People 1.6 2.9 2.4 1.0 0.1
IYS.5 Information Systems 2.2 2.8 2.0 0.8 0.2
IYS.6 Outreach and Communication 0.4 2.6 3.0 1.4 0.6DFO Enhancement Staff
HMjob(y1p, "HA","DFO Pacific: Enhancement Staff")
name region jobCode
2 Al Jonsson Pacific HA
29 Chantal Nessman Pacific HA
34 Dale Desrochers Pacific HA
36 Dave Davies Pacific HA
44 Don MacKinlay Pacific HA
66 James Weger Pacific HA
69 Jason Mahoney Pacific HA
87 Kerra Shaw Pacific HA
136 Rob Schaefer Pacific HA
144 Scott Ducharme Pacific HA
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 0 1 4 1 4
IYS.1.2 Data Analysis 1 2 4 1 2
IYS.1.3 Fishery Management, Assessment 2 0 5 1 2
IYS.1.4 Stock Status Assessment 1 0 5 1 3
IYS.1.5 Habitat Assessment 1 1 0 4 4
IYS.1.6 Population identification 2 1 3 1 3
IYS.1.7 Marine Survival, Growth, Migration 3 0 2 2 3
IYS.1.8 Interactions: Wild, Hatchery, Farmed 2 1 0 0 7
IYS.1.9 Toxicology 3 2 3 1 1
IYS.2.1 Freshwater habitats 2 1 3 2 2
IYS.2.2 Marine and Estuarine Habitats 1 2 3 2 2
IYS.2.3 Climate and Ecosystem Models 2 2 4 2 0
IYS.2.4 Adaptation 1 4 2 2 1
IYS.2.5 Policy and Management 2 4 2 0 2
IYS.3.1 Field methods 0 2 3 2 3
IYS.3.2 Individual fish 2 2 3 1 2
IYS.3.3 Fisheries management process 3 3 2 1 1
IYS.3.4 New analyses 4 3 2 1 0
IYS.3.5 Advances genetics, genomics 2 2 4 1 1
IYS.3.6 Science management 3 1 2 2 2
IYS.3.7 Implementation 3 1 1 2 3
IYS.4.1 First Nations Opportunities 1 1 5 1 2
IYS.4.2 Benefits from Salmon 1 3 3 1 2
IYS.4.3 Community engagement 0 1 3 1 5
IYS.4.4 Better science communication 1 3 1 1 4
IYS.4.5 Traditional ecological knowledge 1 3 4 2 0
IYS.4.6 Young scientists 1 2 5 0 2
IYS.4.7 Changing role of salmon in societies 3 4 1 1 1
IYS.5.1 Database Integration 1 2 4 0 3
IYS.5.2 Knowledge management 1 3 4 1 1
IYS.5.3 Data sharing arrangements 0 3 4 1 2
IYS.5.4 Data visualization 2 2 4 0 2
IYS.6.1 International projects 2 3 4 1 0
IYS.6.2 Celebrating success 0 2 2 2 4
IYS.6.3 Outreach methods, awareness 0 1 3 1 5
IYS.6.4 Engagement FM to science to FM 3 0 3 2 2
IYS.6.5 Linking salmon to climate change 1 2 3 1 3
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 1.7 0.9 2.9 1.3 3.2
IYS.2 Effects of Changing Habitats 1.6 2.6 2.8 1.6 1.4
IYS.3 New tech and methods 2.4 2.0 2.4 1.4 1.7
IYS.4 Connecting Salmon to People 1.1 2.4 3.1 1.0 2.3
IYS.5 Information Systems 1.0 2.5 4.0 0.5 2.0
IYS.6 Outreach and Communication 1.2 1.6 3.0 1.4 2.8DFO Engineering and Technical Staff
HMjob(y1p, "EG","DFO Pacific: Engineering and Technical Staff")
name region jobCode
3 Aleta Rushton Pacific EG
6 Andrew Campbell Pacific EG
20 Brian Leaf Pacific EG
24 Bruce Baxter Pacific EG
32 Colin Nettles Pacific EG
46 Eamon Miyagi Pacific EG
67 Jason Evans Pacific EG
80 Julia Bradshaw Pacific EG
103 Marilyn Helin Pacific EG
104 Marina Milligan Pacific EG
122 Nicholas Komick Pacific EG
140 Sandy Devcic Pacific EG
147 Shaun Spenard Pacific EG
150 Stefan Howarth Pacific EG
156 Stuart LePage Pacific EG
159 Tracy Cone Pacific EG
[,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data 0 2 4 3 7
IYS.1.2 Data Analysis 1 3 7 2 3
IYS.1.3 Fishery Management, Assessment 2 6 3 3 2
IYS.1.4 Stock Status Assessment 4 5 3 3 1
IYS.1.5 Habitat Assessment 4 6 2 2 2
IYS.1.6 Population identification 3 4 6 2 1
IYS.1.7 Marine Survival, Growth, Migration 6 3 4 3 0
IYS.1.8 Interactions: Wild, Hatchery, Farmed 4 3 5 3 1
IYS.1.9 Toxicology 7 6 3 0 0
IYS.2.1 Freshwater habitats 3 6 4 1 2
IYS.2.2 Marine and Estuarine Habitats 6 5 2 2 1
IYS.2.3 Climate and Ecosystem Models 4 5 5 2 0
IYS.2.4 Adaptation 5 4 3 2 2
IYS.2.5 Policy and Management 6 2 6 2 0
IYS.3.1 Field methods 1 4 3 3 5
IYS.3.2 Individual fish 1 3 5 5 2
IYS.3.3 Fisheries management process 6 3 4 2 1
IYS.3.4 New analyses 3 4 5 3 1
IYS.3.5 Advances genetics, genomics 7 2 4 3 0
IYS.3.6 Science management 5 2 3 4 2
IYS.3.7 Implementation 3 2 4 6 1
IYS.4.1 First Nations Opportunities 3 3 6 2 2
IYS.4.2 Benefits from Salmon 5 4 5 1 1
IYS.4.3 Community engagement 3 4 3 3 3
IYS.4.4 Better science communication 3 4 4 3 2
IYS.4.5 Traditional ecological knowledge 3 7 3 2 1
IYS.4.6 Young scientists 4 5 4 2 1
IYS.4.7 Changing role of salmon in societies 6 5 2 2 1
IYS.5.1 Database Integration 0 4 6 4 2
IYS.5.2 Knowledge management 0 5 6 4 1
IYS.5.3 Data sharing arrangements 0 5 5 4 2
IYS.5.4 Data visualization 0 4 8 3 1
IYS.6.1 International projects 5 5 3 1 2
IYS.6.2 Celebrating success 4 6 2 1 3
IYS.6.3 Outreach methods, awareness 5 4 3 1 3
IYS.6.4 Engagement FM to science to FM 4 5 2 2 3
IYS.6.5 Linking salmon to climate change 5 6 1 2 2
Choice
Theme no pending need offer critical
IYS.1 Status Salmon and Habitats 3.4 4.2 4.1 2.3 1.9
IYS.2 Effects of Changing Habitats 4.8 4.4 4.0 1.8 1.0
IYS.3 New tech and methods 3.7 2.9 4.0 3.7 1.7
IYS.4 Connecting Salmon to People 3.9 4.6 3.9 2.1 1.6
IYS.5 Information Systems 0.0 4.5 6.2 3.8 1.5
IYS.6 Outreach and Communication 4.6 5.2 2.2 1.4 2.6Scores by Job: Comparing the Atlantic Salmon Regions to the Pacific Region
As previously for DFO Canada-wide: weighted sums of choices for IYS topics, for each job type. It is necessary to correct the scores for differing numbers of people in each job type (MA, EG, RM, PO, BI, RE, HA).
The jobType PO was not included because it was insufficiently represented (2 responses). This same thing applies to RM for Atlantic (1 response from 4 regions), so that job type was not included in the Atlantic results.
Add a column that is the mean for “ALL” job types. This is not a weighted mean, the rationale is that each job type is equally important to DFO (boat don’t float without techs and scientists and managers).
# x3 is the previous score for all 124 responses, vector 37
# x1 is 124 by 37
# y1p is Pacific region only, 90 by 40, with job,region, name
# y1a is fourAtlantic regions, 31 by 40, with job,region, name
jc=jobCode$code[-4] # delete PO
PacCountJob <- numeric(6); names(PacCountJob) <- jc; AtlCountJob <- PacCountJob
PacScoreJob <- matrix(NA,nrow=37,ncol=7,dimnames=list(Topic=topic, Job=c(jc,"All"))); AtlScoreJob <- PacScoreJob
# Pacific
for(j in 1:6){
hasJob <- y1p$jobCode == jc[j] # who has job
PacCountJob[j] <- sum(hasJob)
a <- y1p[hasJob,1:37] %>% apply(2,ChoiceTabSum); # count choices, 5 by 37
PacScoreJob[,j] <- t(a) %*% (0:4) # weighted sum of choices
PacScoreJob[,j] <-PacScoreJob[,j]/PacCountJob[j] # correct for sample size
}
PacScoreJob[,7] <- apply(PacScoreJob[,1:6],1,mean)
print(round(PacScoreJob,1))
cat("\nCount of Pacific job types",PacCountJob, "\nsum =",
sum(PacCountJob),"\n")
# Atlantic
for(j in 1:6){
hasJob <- y1a$jobCode == jc[j] # who has job
AtlCountJob[j] <- sum(hasJob)
a <- y1a[hasJob,1:37] %>% apply(2,ChoiceTabSum); # count choices, 5 by 37
AtlScoreJob[,j] <- t(a) %*% (0:4) # weighted sum of choices
AtlScoreJob[,j] <-AtlScoreJob[,j]/AtlCountJob[j] # correct for sample size
}
AtlScoreJob[,7] <- apply(AtlScoreJob[,c(1,2, 4,5,6)],1,mean) # mean by of job scores
print(round(AtlScoreJob[,c(1,2, 4,5,6,7)],1))
cat("\nCount of Atlantic job types",AtlCountJob, "\nsum =", sum(AtlCountJob),"\n") Job
Topic MA EG RM BI RE HA All
IYS.1.1 Field Data 1.9 2.9 2.3 1.6 2.0 2.8 2.3
IYS.1.2 Data Analysis 1.3 2.2 2.0 2.4 1.6 2.1 1.9
IYS.1.3 Fishery Management, Assessment 1.4 1.8 2.6 1.9 1.1 2.1 1.8
IYS.1.4 Stock Status Assessment 1.6 1.5 1.7 1.8 1.2 2.5 1.7
IYS.1.5 Habitat Assessment 1.1 1.5 1.2 0.9 2.0 2.9 1.6
IYS.1.6 Population identification 1.4 1.6 1.6 0.8 1.9 2.2 1.6
IYS.1.7 Marine Survival, Growth, Migration 1.3 1.2 1.8 1.8 2.4 2.2 1.8
IYS.1.8 Interactions: Wild, Hatchery, Farmed 0.7 1.6 1.2 1.2 2.6 2.9 1.7
IYS.1.9 Toxicology 0.3 0.8 0.9 0.2 0.9 1.5 0.8
IYS.2.1 Freshwater habitats 1.6 1.6 1.6 1.1 1.5 2.1 1.6
IYS.2.2 Marine and Estuarine Habitats 1.4 1.2 1.6 1.4 1.9 2.2 1.6
IYS.2.3 Climate and Ecosystem Models 0.6 1.3 1.3 1.3 2.2 1.6 1.4
IYS.2.4 Adaptation 0.6 1.5 1.4 1.1 1.9 1.8 1.4
IYS.2.5 Policy and Management 1.3 1.2 1.8 1.2 1.2 1.6 1.4
IYS.3.1 Field methods 1.6 2.4 2.1 1.1 1.4 2.6 1.9
IYS.3.2 Individual fish 1.1 2.2 1.3 1.1 1.5 1.9 1.5
IYS.3.3 Fisheries management process 1.1 1.3 2.1 1.6 0.8 1.4 1.4
IYS.3.4 New analyses 0.9 1.7 1.4 1.4 1.4 1.0 1.3
IYS.3.5 Advances genetics, genomics 1.1 1.2 1.3 0.7 1.8 1.7 1.3
IYS.3.6 Science management 1.3 1.8 1.4 1.4 1.8 1.9 1.6
IYS.3.7 Implementation 1.3 2.0 1.7 1.8 2.4 2.1 1.9
IYS.4.1 First Nations Opportunities 2.4 1.8 2.4 1.2 1.5 2.2 1.9
IYS.4.2 Benefits from Salmon 2.0 1.3 2.3 0.8 1.1 2.0 1.6
IYS.4.3 Community engagement 1.4 1.9 2.0 1.1 1.4 3.0 1.8
IYS.4.4 Better science communication 1.9 1.8 2.4 1.8 2.1 2.4 2.1
IYS.4.5 Traditional ecological knowledge 2.3 1.4 1.6 0.8 0.9 1.7 1.4
IYS.4.6 Young scientists 1.7 1.4 1.7 0.8 2.1 2.0 1.6
IYS.4.7 Changing role of salmon in societies 0.7 1.2 1.3 0.7 0.8 1.3 1.0
IYS.5.1 Database Integration 1.3 2.2 1.9 1.8 1.4 2.2 1.8
IYS.5.2 Knowledge management 1.4 2.1 1.7 1.7 1.2 1.8 1.7
IYS.5.3 Data sharing arrangements 1.6 2.2 1.9 1.9 1.0 2.2 1.8
IYS.5.4 Data visualization 1.3 2.1 1.5 2.0 1.4 1.8 1.7
IYS.6.1 International projects 1.4 1.4 1.2 1.1 1.9 1.4 1.4
IYS.6.2 Celebrating success 1.9 1.6 1.4 0.9 1.9 2.8 1.7
IYS.6.3 Outreach methods, awareness 1.6 1.6 1.6 0.6 2.1 3.0 1.7
IYS.6.4 Engagement FM to science to FM 1.9 1.7 2.3 1.6 1.6 2.0 1.8
IYS.6.5 Linking salmon to climate change 1.4 1.4 1.7 1.6 2.0 2.3 1.7
Count of Pacific job types 7 16 31 16 8 10
sum = 88
Job
Topic MA EG BI RE HA All
IYS.1.1 Field Data 1.5 3.2 2.6 1.8 2.8 2.4
IYS.1.2 Data Analysis 1.5 2.0 2.1 1.6 2.4 1.9
IYS.1.3 Fishery Management, Assessment 2.5 3.2 1.3 0.2 2.6 2.0
IYS.1.4 Stock Status Assessment 2.0 2.8 1.1 0.6 2.6 1.8
IYS.1.5 Habitat Assessment 1.2 2.2 1.2 1.0 2.8 1.7
IYS.1.6 Population identification 2.0 2.5 1.6 0.8 2.8 1.9
IYS.1.7 Marine Survival, Growth, Migration 2.0 3.2 2.0 2.0 2.8 2.4
IYS.1.8 Interactions: Wild, Hatchery, Farmed 1.2 3.0 1.7 1.6 2.8 2.1
IYS.1.9 Toxicology 1.2 1.0 0.4 0.4 1.8 1.0
IYS.2.1 Freshwater habitats 1.5 2.2 2.0 0.4 1.6 1.6
IYS.2.2 Marine and Estuarine Habitats 1.5 1.8 1.8 1.4 1.6 1.6
IYS.2.3 Climate and Ecosystem Models 1.2 1.2 1.5 1.2 1.4 1.3
IYS.2.4 Adaptation 1.5 1.5 1.9 1.0 1.4 1.5
IYS.2.5 Policy and Management 2.2 1.8 1.1 0.2 1.4 1.3
IYS.3.1 Field methods 1.8 3.2 2.5 1.6 2.2 2.3
IYS.3.2 Individual fish 1.0 2.8 2.2 0.8 2.2 1.8
IYS.3.3 Fisheries management process 2.5 1.8 1.0 0.2 2.2 1.5
IYS.3.4 New analyses 1.5 1.0 1.3 1.0 1.8 1.3
IYS.3.5 Advances genetics, genomics 1.5 1.2 1.3 0.8 1.8 1.3
IYS.3.6 Science management 3.0 2.0 1.5 1.0 2.2 1.9
IYS.3.7 Implementation 2.5 2.5 1.9 1.6 2.2 2.1
IYS.4.1 First Nations Opportunities 3.0 3.2 2.4 0.6 2.2 2.3
IYS.4.2 Benefits from Salmon 2.8 2.5 1.7 0.6 2.2 1.9
IYS.4.3 Community engagement 2.8 2.5 2.0 0.6 2.2 2.0
IYS.4.4 Better science communication 2.8 2.5 2.3 0.8 2.2 2.1
IYS.4.5 Traditional ecological knowledge 2.5 2.5 1.6 0.2 2.2 1.8
IYS.4.6 Young scientists 2.0 2.5 1.3 1.2 1.8 1.8
IYS.4.7 Changing role of salmon in societies 2.2 1.5 0.8 0.4 1.8 1.4
IYS.5.1 Database Integration 1.5 2.0 1.5 1.2 2.0 1.6
IYS.5.2 Knowledge management 1.8 1.8 1.1 1.0 2.0 1.5
IYS.5.3 Data sharing arrangements 1.2 2.2 1.4 1.6 2.0 1.7
IYS.5.4 Data visualization 1.2 1.0 1.5 1.4 2.0 1.4
IYS.6.1 International projects 2.0 1.2 1.8 1.6 1.6 1.7
IYS.6.2 Celebrating success 2.2 2.2 1.9 0.8 1.6 1.8
IYS.6.3 Outreach methods, awareness 2.0 1.8 1.8 1.2 1.6 1.7
IYS.6.4 Engagement FM to science to FM 2.5 2.0 1.9 0.4 1.6 1.7
IYS.6.5 Linking salmon to climate change 1.5 2.0 1.5 1.0 1.6 1.5
Count of Atlantic job types 4 4 1 12 5 5
sum = 31 PacThemeScoreJob <-matrix(NA,6,7,dimnames=list(Theme=theme,JobCode=c(jc,"ALL")))
AtlThemeScoreJob <- PacThemeScoreJob
# Pacific
for(j in 1:6) PacThemeScoreJob[,j] = tapply(PacScoreJob[,j],fctr,mean)
PacThemeScoreJob[,7]= apply(PacThemeScoreJob[,1:6],1, mean)
round(PacThemeScoreJob,1)
# replace with scaled version. Note ALL is mean AFTER scaling, mean of scaled.
PacThemeScoreJob[,1:6] <- apply(PacThemeScoreJob[,1:6], 2, ScaleTo10)
PacThemeScoreJob[,7] <- apply(PacThemeScoreJob[,1:6], 1, mean) %>% round(0)
PacThemeScoreJob %>% kable(caption="Pacific")
# Atlantic
for(j in 1:6) AtlThemeScoreJob[,j] = tapply(AtlScoreJob[,j],fctr,mean)
AtlThemeScoreJob[,7] <- apply(AtlThemeScoreJob[,c(1,2,4:6)],1, mean) # not RM
round(AtlThemeScoreJob[,c(1,2,4:7)],1)
AtlThemeScoreJob <- apply(AtlThemeScoreJob, 2, ScaleTo10)
AtlThemeScoreJob[,7] <- apply(AtlThemeScoreJob[,c(1,2, 4,5,6)], 1, mean) %>% round(0) # not RM
AtlThemeScoreJob[,c(1,2, 4:7)] %>% kable(caption="Atlantic") JobCode
Theme MA EG RM BI RE HA ALL
IYS.1 Status Salmon and Habitats 1.2 1.7 1.7 1.4 1.8 2.4 1.7
IYS.2 Effects of Changing Habitats 1.1 1.4 1.5 1.2 1.8 1.9 1.5
IYS.3 New tech and methods 1.2 1.8 1.6 1.3 1.6 1.8 1.5
IYS.4 Connecting Salmon to People 1.8 1.6 2.0 1.0 1.4 2.1 1.6
IYS.5 Information Systems 1.4 2.1 1.8 1.8 1.2 2.0 1.7
IYS.6 Outreach and Communication 1.6 1.5 1.6 1.2 1.9 2.3 1.7| MA | EG | RM | BI | RE | HA | ALL | |
|---|---|---|---|---|---|---|---|
| IYS.1 Status Salmon and Habitats | 2 | 4 | 4 | 5 | 8 | 10 | 6 |
| IYS.2 Effects of Changing Habitats | 0 | 0 | 0 | 3 | 8 | 1 | 2 |
| IYS.3 New tech and methods | 2 | 6 | 1 | 3 | 5 | 0 | 3 |
| IYS.4 Connecting Salmon to People | 10 | 3 | 10 | 0 | 2 | 5 | 5 |
| IYS.5 Information Systems | 4 | 10 | 5 | 10 | 0 | 4 | 6 |
| IYS.6 Outreach and Communication | 8 | 2 | 2 | 2 | 10 | 9 | 6 |
JobCode
Theme MA EG BI RE HA ALL
IYS.1 Status Salmon and Habitats 1.7 2.6 1.6 1.1 2.6 1.9
IYS.2 Effects of Changing Habitats 1.6 1.7 1.7 0.8 1.5 1.5
IYS.3 New tech and methods 2.0 2.1 1.7 1.0 2.1 1.8
IYS.4 Connecting Salmon to People 2.6 2.5 1.7 0.6 2.1 1.9
IYS.5 Information Systems 1.4 1.8 1.4 1.3 2.0 1.6
IYS.6 Outreach and Communication 2.0 1.8 1.8 1.0 1.6 1.7| MA | EG | BI | RE | HA | ALL | |
|---|---|---|---|---|---|---|
| IYS.1 Status Salmon and Habitats | 2 | 10 | 4 | 7 | 10 | 7 |
| IYS.2 Effects of Changing Habitats | 1 | 0 | 7 | 3 | 0 | 2 |
| IYS.3 New tech and methods | 5 | 4 | 7 | 6 | 5 | 5 |
| IYS.4 Connecting Salmon to People | 10 | 9 | 9 | 0 | 5 | 7 |
| IYS.5 Information Systems | 0 | 1 | 0 | 10 | 5 | 3 |
| IYS.6 Outreach and Communication | 5 | 2 | 10 | 6 | 1 | 5 |
Topic Scaling
For the Atlantic and Pacific groups,job scores by topics were scaled 1 to 10 (not ranked). This had to be done after aggregating to themes.
PSJS <- apply(PacScoreJob, 2, ScaleTo10) # within columns.
PSJS[,7] <- PSJS[,1:6] %>% apply(1,mean) %>% round(0)
PSJS[order(PSJS[,7], decreasing=TRUE),] %>% kable # sort by overall interest
# Atlanic
ASJS <- apply(AtlScoreJob, 2, ScaleTo10) # within columns.
ASJS[,7] <- ASJS[,c(1,2,4:6)] %>% apply(1,mean) %>% round(0)
ASJS[order(ASJS[,7], decreasing=TRUE), c(1,2,4:7) ] %>% kable # sort by overall interest| MA | EG | RM | BI | RE | HA | All | |
|---|---|---|---|---|---|---|---|
| IYS.1.1 Field Data | 7 | 10 | 8 | 6 | 7 | 9 | 8 |
| IYS.1.2 Data Analysis | 5 | 7 | 7 | 10 | 5 | 6 | 7 |
| IYS.4.4 Better science communication | 7 | 5 | 8 | 7 | 7 | 7 | 7 |
| IYS.1.3 Fishery Management, Assessment | 5 | 5 | 10 | 8 | 2 | 6 | 6 |
| IYS.1.7 Marine Survival, Growth, Migration | 5 | 2 | 5 | 7 | 9 | 6 | 6 |
| IYS.3.1 Field methods | 6 | 8 | 7 | 4 | 3 | 8 | 6 |
| IYS.3.7 Implementation | 5 | 6 | 5 | 7 | 9 | 6 | 6 |
| IYS.4.1 First Nations Opportunities | 10 | 5 | 9 | 5 | 4 | 6 | 6 |
| IYS.4.3 Community engagement | 5 | 5 | 7 | 4 | 3 | 10 | 6 |
| IYS.5.1 Database Integration | 5 | 7 | 6 | 7 | 3 | 6 | 6 |
| IYS.5.3 Data sharing arrangements | 6 | 7 | 6 | 8 | 1 | 6 | 6 |
| IYS.6.3 Outreach methods, awareness | 6 | 4 | 4 | 2 | 7 | 10 | 6 |
| IYS.6.4 Engagement FM to science to FM | 7 | 4 | 8 | 6 | 5 | 5 | 6 |
| IYS.1.4 Stock Status Assessment | 6 | 3 | 5 | 7 | 3 | 8 | 5 |
| IYS.1.5 Habitat Assessment | 4 | 3 | 2 | 3 | 7 | 10 | 5 |
| IYS.1.6 Population identification | 5 | 4 | 4 | 3 | 6 | 6 | 5 |
| IYS.1.8 Interactions: Wild, Hatchery, Farmed | 2 | 4 | 2 | 4 | 10 | 10 | 5 |
| IYS.2.1 Freshwater habitats | 6 | 4 | 4 | 4 | 4 | 6 | 5 |
| IYS.2.2 Marine and Estuarine Habitats | 5 | 2 | 4 | 6 | 6 | 6 | 5 |
| IYS.3.6 Science management | 5 | 5 | 3 | 6 | 5 | 4 | 5 |
| IYS.4.2 Benefits from Salmon | 8 | 3 | 8 | 2 | 2 | 5 | 5 |
| IYS.4.6 Young scientists | 7 | 3 | 5 | 3 | 7 | 5 | 5 |
| IYS.5.2 Knowledge management | 5 | 6 | 5 | 7 | 3 | 4 | 5 |
| IYS.5.4 Data visualization | 5 | 6 | 3 | 8 | 3 | 4 | 5 |
| IYS.6.2 Celebrating success | 7 | 4 | 2 | 3 | 6 | 9 | 5 |
| IYS.6.5 Linking salmon to climate change | 5 | 3 | 4 | 6 | 7 | 6 | 5 |
| IYS.2.3 Climate and Ecosystem Models | 1 | 3 | 2 | 5 | 8 | 3 | 4 |
| IYS.2.5 Policy and Management | 5 | 2 | 5 | 5 | 3 | 3 | 4 |
| IYS.3.2 Individual fish | 4 | 7 | 2 | 4 | 4 | 4 | 4 |
| IYS.3.3 Fisheries management process | 4 | 3 | 7 | 6 | 0 | 2 | 4 |
| IYS.4.5 Traditional ecological knowledge | 9 | 3 | 4 | 2 | 1 | 4 | 4 |
| IYS.6.1 International projects | 5 | 3 | 1 | 4 | 6 | 2 | 4 |
| IYS.2.4 Adaptation | 1 | 3 | 2 | 4 | 6 | 4 | 3 |
| IYS.3.4 New analyses | 3 | 4 | 2 | 6 | 3 | 0 | 3 |
| IYS.3.5 Advances genetics, genomics | 4 | 2 | 2 | 2 | 5 | 4 | 3 |
| IYS.4.7 Changing role of salmon in societies | 2 | 2 | 2 | 2 | 0 | 2 | 2 |
| IYS.1.9 Toxicology | 0 | 0 | 0 | 0 | 1 | 2 | 0 |
| MA | EG | BI | RE | HA | All | |
|---|---|---|---|---|---|---|
| IYS.1.1 Field Data | 2 | 10 | 10 | 9 | 10 | 8 |
| IYS.1.7 Marine Survival, Growth, Migration | 5 | 10 | 7 | 10 | 10 | 8 |
| IYS.3.1 Field methods | 4 | 10 | 10 | 8 | 6 | 8 |
| IYS.1.8 Interactions: Wild, Hatchery, Farmed | 1 | 9 | 6 | 8 | 10 | 7 |
| IYS.3.7 Implementation | 8 | 7 | 7 | 8 | 6 | 7 |
| IYS.4.1 First Nations Opportunities | 10 | 10 | 9 | 2 | 6 | 7 |
| IYS.4.4 Better science communication | 9 | 7 | 9 | 3 | 6 | 7 |
| IYS.1.2 Data Analysis | 2 | 4 | 8 | 8 | 7 | 6 |
| IYS.1.3 Fishery Management, Assessment | 8 | 10 | 4 | 0 | 9 | 6 |
| IYS.1.6 Population identification | 5 | 7 | 5 | 3 | 10 | 6 |
| IYS.3.6 Science management | 10 | 4 | 5 | 4 | 6 | 6 |
| IYS.4.2 Benefits from Salmon | 9 | 7 | 6 | 2 | 6 | 6 |
| IYS.4.3 Community engagement | 9 | 7 | 7 | 2 | 6 | 6 |
| IYS.1.4 Stock Status Assessment | 5 | 8 | 3 | 2 | 9 | 5 |
| IYS.1.5 Habitat Assessment | 1 | 6 | 4 | 4 | 10 | 5 |
| IYS.3.2 Individual fish | 0 | 8 | 8 | 3 | 6 | 5 |
| IYS.4.5 Traditional ecological knowledge | 8 | 7 | 5 | 0 | 6 | 5 |
| IYS.4.6 Young scientists | 5 | 7 | 4 | 6 | 3 | 5 |
| IYS.5.3 Data sharing arrangements | 1 | 6 | 5 | 8 | 4 | 5 |
| IYS.6.2 Celebrating success | 6 | 6 | 7 | 3 | 1 | 5 |
| IYS.2.2 Marine and Estuarine Habitats | 2 | 3 | 7 | 7 | 1 | 4 |
| IYS.3.3 Fisheries management process | 8 | 3 | 3 | 0 | 6 | 4 |
| IYS.5.1 Database Integration | 2 | 4 | 5 | 6 | 4 | 4 |
| IYS.5.2 Knowledge management | 4 | 3 | 3 | 4 | 4 | 4 |
| IYS.6.1 International projects | 5 | 1 | 7 | 8 | 1 | 4 |
| IYS.6.3 Outreach methods, awareness | 5 | 3 | 7 | 6 | 1 | 4 |
| IYS.6.4 Engagement FM to science to FM | 8 | 4 | 7 | 1 | 1 | 4 |
| IYS.2.1 Freshwater habitats | 2 | 6 | 7 | 1 | 1 | 3 |
| IYS.2.3 Climate and Ecosystem Models | 1 | 1 | 5 | 6 | 0 | 3 |
| IYS.2.4 Adaptation | 2 | 2 | 7 | 4 | 0 | 3 |
| IYS.3.4 New analyses | 2 | 0 | 4 | 4 | 3 | 3 |
| IYS.3.5 Advances genetics, genomics | 2 | 1 | 4 | 3 | 3 | 3 |
| IYS.4.7 Changing role of salmon in societies | 6 | 2 | 2 | 1 | 3 | 3 |
| IYS.5.4 Data visualization | 1 | 0 | 5 | 7 | 4 | 3 |
| IYS.6.5 Linking salmon to climate change | 2 | 4 | 5 | 4 | 1 | 3 |
| IYS.2.5 Policy and Management | 6 | 3 | 3 | 0 | 0 | 2 |
| IYS.1.9 Toxicology | 1 | 0 | 0 | 1 | 3 | 1 |
Top Topics Plot
The scores for IYS topics reflect interest in particpating in collaboration, something that cannot be entirely separated from personal (non-colaborative) interest or perceived future value regarding an IYS topic. To compare and contrast this interest among job types, we asked: For topics with the highest scores for one or more job types (within Pacific Region), how does that topic score for other job types? Examination of the scaled PacScoreJobs produced 16 such topics. We observed a preponderance of topics from IYS themes 1 (current status of salmon) and 4 (connecting salmon to people), in contrast to themes 2 (future), 3 (new tech), and 5 (information systems). The top interests in collaboration for each job type in Pacific were applied to Atlantic fo comparison. OLD 1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1,4.2,4.3, 4.5,5.3, 6.3,6.4 NEW 1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1, 4.3,4.4,4.5,5.3,6.2,6.3
Pacific Region
# (1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1, 4.2,4.3,4.5,5.3,6.3,6.4) # 16
tops <- as.character(c(1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1, 4.3,4.4,4.5,5.3,6.2,6.3))
nt <- length(tops)
top <- which(substring(topic,5,7) %in% tops)
# 1 2 3 5 7 8 12 15 21 22 24 25 26 31 34 35 which of 37 topics
jc= jobCode[-4,1] # 6 without PO
mainText=substring(topic[top],9) %T>% print # from nineth character on
SetPar();par(xaxs="r");
par(mfcol=c(4,4), mar=c(1,1,1,0) );
for(j in 1:nt){
y=PSJS[top[j],1:6] # how job types responded to a topic
plot(1:6,y, xlim=c(1,6),ylim=c(-1,11),xaxt="n",yaxt="n",
xlab="", ylab="",main=mainText[j], cex.main=0.8);
m=mean(y); abline(h=m) # a horizontal line at the mean
segments(1:6, rep(m,6),1:6,y) # connect dot to horizontal line
axis(3,labels=F); axis(4,at=0:9,labels=F)
if(j < 5) {axis(2,at=0:10,labels=c("0",NA,NA,NA,NA,"5",NA,NA,NA,NA,"10"), las=1) # right hand
} else {axis(2,at=0:10,labels=F)}
if((j %% 4)==0){axis(1,at=1:6,labels=jc)} else {axis(1,labels=F)}; # bottom by modular arithmetic
}
mtext("Collaboration Interest Score",2, outer=T,line=0.5)
mtext("Job Type", 1, outer=T,line=0.75)
mtext("Pacific Salmon", 3, outer=T,line=-0.25)
[1] "Field Data"
[2] "Data Analysis"
[3] "Fishery Management, Assessment"
[4] "Habitat Assessment"
[5] "Marine Survival, Growth, Migration"
[6] "Interactions: Wild, Hatchery, Farmed"
[7] "Climate and Ecosystem Models"
[8] "Field methods"
[9] "Implementation"
[10] "First Nations Opportunities"
[11] "Community engagement"
[12] "Better science communication"
[13] "Traditional ecological knowledge"
[14] "Data sharing arrangements"
[15] "Celebrating success"
[16] "Outreach methods, awareness" Atlantic Region
The same topics were applied to the four regions with Atlantic Salmon. That lumped: Quebec, Gulf, Maritimes, Newfoundland. Job type RM was omitted (1 response).
SetPar();par(xaxs="r");
par(mfcol=c(4,4), mar=c(1,1,1,0) );
for(j in 1:nt){
y=ASJS[top[j],c(1,2,4:6)] # how 5 job types responded to a topic, not RM
plot(1:5,y, xlim=c(1,5),ylim=c(-1,11),xaxt="n",yaxt="n",
xlab="", ylab="",main=mainText[j], cex.main=0.8);
m=mean(y); abline(h=m) # a horizontal line at the mean
segments(1:5, rep(m,5),1:5,y) # connect dot to horizontal line
axis(3,labels=F); axis(4,at=0:9,labels=F)
if(j < 5) {axis(2,at=0:10,labels=c("0",NA,NA,NA,NA,"5",NA,NA,NA,NA,"10"), las=1) # right hand
} else {axis(2,at=0:10,labels=F)}
if((j %% 4)==0){axis(1,at=1:5,labels=jc[c(1,2,4:6)])} else {axis(1,labels=F)}; # bottom by modular arithmetic
}
mtext("Collaboration Interest Score",2, outer=T,line=0.5)
mtext("Job Type", 1, outer=T,line=0.75)
mtext("Atlantic Salmon", 3, outer=T,line=-0.25)
Amazing how different RE is compared to MA in term of indicated strong potential for collaboration.
Atlantic and Pacific
Compare responses between Atlantic and Pacific by job type.
SetPar();par(xaxs="r");
par(mfcol=c(4,4), mar=c(1,1,1,0) );
for(j in 1:nt){
y <- PSJS[top[j],1:6] # how job types responded to a topic
plot(1:6,y, xlim=c(1,6),ylim=c(-1,11),xaxt="n",yaxt="n",
xlab="", ylab="",main=mainText[j], cex.main=0.8);
m=mean(y); abline(h=m) # a horizontal line at the mean
segments(1:6, rep(m,6),1:6,y) # connect dot to horizontal line
y1 <- ASJS[top[j],c(1,2,4:6)] # no RM
points(c(1,2,4:6),y1,pch=5,col="red") # skip jobtype at position 3
m=mean(y1); abline(h=m, col="red")
axis(3,labels=F); axis(4,at=0:9,labels=F)
if(j < 5) {axis(2,at=0:10,labels=c("0",NA,NA,NA,NA,"5",NA,NA,NA,NA,"10"), las=1) # right hand
} else {axis(2,at=0:10,labels=F)}
if((j %% 4)==0){axis(1,at=1:6,labels=jc)} else {axis(1,labels=F)}; # bottom by modular arithmetic
}
mtext("Collaboration Interest Score",2, outer=T,line=0.5 );
mtext("Job Type", 1, outer=T,line=0.75); 
There are some sharp contrasts within job types between Atlantic and Pacific, in perceptions of opportunities for collaboration. Comparing Atlantic to Pacific:
* MA had less interest in field data, habitat assessment, and data sharing arrangements than their Pacific counterparts;
* EG were more intersted in marine survival/growth/migration and in fishery management/assessment;
* BI were very similar but more interested in field methods.
* RE were also similar but more interested in data sharing arrangements. * HA differed by having much less interested in celebrating success and in outreach methods/awareness.
Correlations Among Job Types within Top Topics
cp <- cor(PSJS[top,1:6]) %>% `^`(2) %>% `*`(100) %>% round(0) %T>% print;
ca <- cor(ASJS[top,1:6]) %>% `^`(2) %>% `*`(100) %>% round(0) %T>% print;
cb <- cp # the bottom
for (j in 1:5){
for(k in (j+1):6 ){
cb[j,k] <- ca[j,k];
if( j == 3 | k == 3) cb[j,k] <- NA
}
}
for (j in 1:6){
cb[j,j] <- cor(PSJS[top,j], ASJS[top,j]) %>% `^`(2) %>% `*`(100) %>% round(0)
}
cb[3,3] <- NA
cb MA EG RM BI RE HA
MA 100 5 23 1 25 0
EG 5 100 32 16 6 2
RM 23 32 100 30 22 2
BI 1 16 30 100 0 16
RE 25 6 22 0 100 5
HA 0 2 2 16 5 100
MA EG RM BI RE HA
MA 100 10 14 4 41 0
EG 10 100 0 11 0 51
RM 14 0 100 0 18 9
BI 4 11 0 100 11 0
RE 41 0 18 11 100 2
HA 0 51 9 0 2 100
MA EG RM BI RE HA
MA 36 10 NA 4 41 0
EG 5 8 NA 11 0 51
RM 23 32 NA NA NA NA
BI 1 16 30 1 11 0
RE 25 6 22 0 27 2
HA 0 2 2 16 5 5Selected Topics for Themes
repr=c(0,0,0,1,1,0,0,0,0,2,2,0,0,0,3,0,0,0,0,0,3,4,0,4,0,0,0,0,5,0,5,0,0,0,6,0,6)
topic[repr!=0] [1] "IYS.1.4 Stock Status Assessment"
[2] "IYS.1.5 Habitat Assessment"
[3] "IYS.2.1 Freshwater habitats"
[4] "IYS.2.2 Marine and Estuarine Habitats"
[5] "IYS.3.1 Field methods"
[6] "IYS.3.7 Implementation"
[7] "IYS.4.1 First Nations Opportunities"
[8] "IYS.4.3 Community engagement"
[9] "IYS.5.1 Database Integration"
[10] "IYS.5.3 Data sharing arrangements"
[11] "IYS.6.3 Outreach methods, awareness"
[12] "IYS.6.5 Linking salmon to climate change"Two Topics per Theme
An experiment. ranking within job types was similar to considering all topics. Reducing the amount of information to describe collaboration interest by theme was interesting, but not included in the final analysis. EDA!
reprTopicScores <- matrix(nrow=6,ncol=7)
jc=c(jobCode[c(1:3,5:7),1],"ALL")
for(j in 1:7) reprTopicScores[,j] <- tapply(PacScoreJob[,j],repr,mean)[-1]
round(reprTopicScores,1)
a <- reprTopicScores+NA
dimnames(a)=list(Theme=theme,JobCode=jc)
for(j in 1:7)a[,j] <- ScaleTo10(reprTopicScores[,j])
kable( a) # ,col.names=c(jc,"ALL"), row.names=theme ) [,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 1.4 1.5 1.5 1.4 1.6 2.7 1.7
[2,] 1.5 1.4 1.6 1.3 1.7 2.2 1.6
[3,] 1.4 2.2 1.9 1.4 1.9 2.4 1.9
[4,] 1.9 1.9 2.2 1.2 1.4 2.6 1.9
[5,] 1.4 2.2 1.9 1.8 1.2 2.2 1.8
[6,] 1.5 1.5 1.6 1.1 2.1 2.6 1.7| MA | EG | RM | BI | RE | HA | ALL | |
|---|---|---|---|---|---|---|---|
| IYS.1 Status Salmon and Habitats | 0 | 1 | 0 | 3 | 5 | 10 | 2 |
| IYS.2 Effects of Changing Habitats | 2 | 0 | 2 | 2 | 6 | 0 | 0 |
| IYS.3 New tech and methods | 1 | 10 | 6 | 4 | 8 | 4 | 10 |
| IYS.4 Connecting Salmon to People | 10 | 6 | 10 | 1 | 3 | 8 | 10 |
| IYS.5 Information Systems | 1 | 10 | 6 | 10 | 0 | 1 | 7 |
| IYS.6 Outreach and Communication | 2 | 1 | 2 | 0 | 10 | 9 | 5 |
finis